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Wu T, Yu JC, Suresh A, Gale-Day ZJ, Alteen MG, Woo AS, Millbern Z, Johnson OT, Carroll EC, Partch CL, Fourches D, Vinueza NR, Vocadlo DJ, Gestwicki JE. Conformationally responsive dyes enable protein-adaptive differential scanning fluorimetry. bioRxiv 2023:2023.01.23.525251. [PMID: 36747624 PMCID: PMC9900766 DOI: 10.1101/2023.01.23.525251] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Flexible in vitro methods alter the course of biological discoveries. Differential Scanning Fluorimetry (DSF) is a particularly versatile technique which reports protein thermal unfolding via fluorogenic dye. However, applications of DSF are limited by widespread protein incompatibilities with the available DSF dyes. Here, we enable DSF applications for 66 of 70 tested proteins (94%) including 10 from the SARS-CoV2 virus using a chemically diverse dye library, Aurora, to identify compatible dye-protein pairs in high throughput. We find that this protein-adaptive DSF platform (paDSF) not only triples the previous protein compatibility, but also fundamentally extends the processes observable by DSF, including interdomain allostery in O-GlcNAc Transferase (OGT). paDSF enables routine measurement of protein stability, dynamics, and ligand binding.
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Affiliation(s)
- Taiasean Wu
- Department of Pharmaceutical Chemistry, University of California San Francisco; San Francisco, CA, 94038, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco; San Francisco, CA, 94038, USA
| | - Joshua C. Yu
- Department of Pharmaceutical Chemistry, University of California San Francisco; San Francisco, CA, 94038, USA
| | - Arundhati Suresh
- Department of Pharmaceutical Chemistry, University of California San Francisco; San Francisco, CA, 94038, USA
| | - Zachary J. Gale-Day
- Department of Pharmaceutical Chemistry, University of California San Francisco; San Francisco, CA, 94038, USA
| | - Matthew G. Alteen
- Department of Chemistry, Simon Fraser University; Burnaby, BC V5A 1S6, Canada
| | - Amanda S. Woo
- Department of Pharmaceutical Chemistry, University of California San Francisco; San Francisco, CA, 94038, USA
| | - Zoe Millbern
- Department of Textile Engineering, North Carolina State University; Raleigh, NC 27695, USA
| | - Oleta T. Johnson
- Institute for Neurodegenerative Diseases, University of California, San Francisco; San Francisco, CA, 94038, USA
| | - Emma C. Carroll
- Institute for Neurodegenerative Diseases, University of California, San Francisco; San Francisco, CA, 94038, USA
| | - Carrie L. Partch
- Department of Chemistry, University of California, Santa Cruz; Santa Cruz, CA, 95064, USA
| | - Denis Fourches
- Department of Textile Engineering, North Carolina State University; Raleigh, NC 27695, USA
| | - Nelson R. Vinueza
- Department of Textile Engineering, North Carolina State University; Raleigh, NC 27695, USA
| | - David J. Vocadlo
- Department of Chemistry, Simon Fraser University; Burnaby, BC V5A 1S6, Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University; Burnaby, BC V5A 1S6, Canada
| | - Jason E. Gestwicki
- Department of Pharmaceutical Chemistry, University of California San Francisco; San Francisco, CA, 94038, USA
- Institute for Neurodegenerative Diseases, University of California, San Francisco; San Francisco, CA, 94038, USA
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2
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Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, Bhattacharya S, Bastos JV, Boyd S, Brown JB, Capuzzi SJ, Chushak Y, Ciallella H, Clark AM, Consonni V, Daga PR, Ekins S, Farag S, Fedorov M, Fourches D, Gadaleta D, Gao F, Gearhart JM, Goh G, Goodman JM, Grisoni F, Grulke CM, Hartung T, Hirn M, Karpov P, Korotcov A, Lavado GJ, Lawless M, Li X, Luechtefeld T, Lunghini F, Mangiatordi GF, Marcou G, Marsh D, Martin T, Mauri A, Muratov EN, Myatt GJ, Nguyen DT, Nicolotti O, Note R, Pande P, Parks AK, Peryea T, Polash AH, Rallo R, Roncaglioni A, Rowlands C, Ruiz P, Russo DP, Sayed A, Sayre R, Sheils T, Siegel C, Silva AC, Simeonov A, Sosnin S, Southall N, Strickland J, Tang Y, Teppen B, Tetko IV, Thomas D, Tkachenko V, Todeschini R, Toma C, Tripodi I, Trisciuzzi D, Tropsha A, Varnek A, Vukovic K, Wang Z, Wang L, Waters KM, Wedlake AJ, Wijeyesakere SJ, Wilson D, Xiao Z, Yang H, Zahoranszky-Kohalmi G, Zakharov AV, Zhang FF, Zhang Z, Zhao T, Zhu H, Zorn KM, Casey W, Kleinstreuer NC. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect 2021; 129:109001. [PMID: 34647794 PMCID: PMC8516060 DOI: 10.1289/ehp10369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 05/21/2023]
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3
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Muratov EN, Amaro R, Andrade CH, Brown N, Ekins S, Fourches D, Isayev O, Kozakov D, Medina-Franco JL, Merz KM, Oprea TI, Poroikov V, Schneider G, Todd MH, Varnek A, Winkler DA, Zakharov AV, Cherkasov A, Tropsha A. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem Soc Rev 2021; 50:9121-9151. [PMID: 34212944 PMCID: PMC8371861 DOI: 10.1039/d0cs01065k] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Indexed: 01/18/2023]
Abstract
COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.
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Affiliation(s)
- Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
| | - Rommie Amaro
- University of California in San DiegoSan DiegoCAUSA
| | | | | | - Sean Ekins
- Collaborations PharmaceuticalsRaleighNCUSA
| | - Denis Fourches
- Department of Chemistry, North Carolina State UniversityRaleighNCUSA
| | - Olexandr Isayev
- Department of Chemistry, Carnegie Melon UniversityPittsburghPAUSA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook UniversityStony BrookNYUSA
| | | | - Kenneth M. Merz
- Department of Chemistry, Michigan State UniversityEast LansingMIUSA
| | - Tudor I. Oprea
- Department of Internal Medicine and UNM Comprehensive Cancer Center, University of New Mexico, AlbuquerqueNMUSA
- Department of Rheumatology and Inflammation Research, Gothenburg UniversitySweden
- Novo Nordisk Foundation Center for Protein Research, University of CopenhagenDenmark
| | | | - Gisbert Schneider
- Institute of Pharmaceutical Sciences, Swiss Federal Institute of TechnologyZurichSwitzerland
| | | | - Alexandre Varnek
- Department of Chemistry, University of StrasbourgStrasbourgFrance
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido UniversitySapporoJapan
| | - David A. Winkler
- Monash Institute of Pharmaceutical Sciences, Monash UniversityMelbourneVICAustralia
- School of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe UniversityBundooraAustralia
- School of Pharmacy, University of NottinghamNottinghamUK
| | | | - Artem Cherkasov
- Vancouver Prostate Centre, University of British ColumbiaVancouverBCCanada
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North CarolinaChapel HillNCUSA
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4
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Mansouri K, Karmaus A, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, Bhattacharya S, Bastos JV, Boyd S, Brown JB, Capuzzi SJ, Chushak Y, Ciallella H, Clark AM, Consonni V, Daga PR, Ekins S, Farag S, Fedorov M, Fourches D, Gadaleta D, Gao F, Gearhart JM, Goh G, Goodman JM, Grisoni F, Grulke CM, Hartung T, Hirn M, Karpov P, Korotcov A, Lavado GJ, Lawless M, Li X, Luechtefeld T, Lunghini F, Mangiatordi GF, Marcou G, Marsh D, Martin T, Mauri A, Muratov EN, Myatt GJ, Nguyen DT, Nicolotti O, Note R, Pande P, Parks AK, Peryea T, Polash A, Rallo R, Roncaglioni A, Rowlands C, Ruiz P, Russo D, Sayed A, Sayre R, Sheils T, Siegel C, Silva AC, Simeonov A, Sosnin S, Southall N, Strickland J, Tang Y, Teppen B, Tetko IV, Thomas D, Tkachenko V, Todeschini R, Toma C, Tripodi I, Trisciuzzi D, Tropsha A, Varnek A, Vukovic K, Wang Z, Wang L, Waters KM, Wedlake AJ, Wijeyesakere SJ, Wilson D, Xiao Z, Yang H, Zahoranszky-Kohalmi G, Zakharov AV, Zhang FF, Zhang Z, Zhao T, Zhu H, Zorn KM, Casey W, Kleinstreuer NC. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect 2021; 129:79001. [PMID: 34242083 PMCID: PMC8270350 DOI: 10.1289/ehp9883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 05/28/2023]
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5
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Day K, Schneible JD, Young AT, Pozdin VA, Van Den Driessche G, Gaffney LA, Prodromou R, Freytes DO, Fourches D, Daniele M, Menegatti S. Photoinduced reconfiguration to control the protein-binding affinity of azobenzene-cyclized peptides. J Mater Chem B 2021; 8:7413-7427. [PMID: 32661544 DOI: 10.1039/d0tb01189d] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The impact of next-generation biorecognition elements (ligands) will be determined by the ability to remotely control their binding activity for a target biomolecule in complex environments. Compared to conventional mechanisms for regulating binding affinity (pH, ionic strength, or chaotropic agents), light provides higher accuracy and rapidity, and is particularly suited for labile targets. In this study, we demonstrate a general method to develop azobenzene-cyclized peptide ligands with light-controlled affinity for target proteins. Light triggers a cis/trans isomerization of the azobenzene, which results in a major structural rearrangement of the cyclic peptide from a non-binding to a binding configuration. Critical to this goal are the ability to achieve efficient photo-isomerization under low light dosage and the temporal stability of both cis and trans isomers. We demonstrated our method by designing photo-switchable peptides targeting vascular cell adhesion marker 1 (VCAM1), a cell marker implicated in stem cell function. Starting from a known VCAM1-binding linear peptide, an ensemble of azobenzene-cyclized variants with selective light-controlled binding were identified by combining in silico design with experimental characterization via spectroscopy and surface plasmon resonance. Variant cycloAZOB[G-VHAKQHRN-K] featured rapid, light-controlled binding of VCAM1 (KD,trans/KD,cis ∼ 130). Biotin-cycloAZOB[G-VHAKQHRN-K] was utilized to label brain microvascular endothelial cells (BMECs), showing co-localization with anti-VCAM1 antibodies in cis configuration and negligible binding in trans configuration.
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Affiliation(s)
- Kevin Day
- Department of Chemical and Biomolecular Engineering, North Carolina State University, 911 Partners Way, Raleigh, North Carolina, USA.
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6
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Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TE, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, Bhattacharya S, Bastos JV, Boyd S, Brown J, Capuzzi SJ, Chushak Y, Ciallella H, Clark AM, Consonni V, Daga PR, Ekins S, Farag S, Fedorov M, Fourches D, Gadaleta D, Gao F, Gearhart JM, Goh G, Goodman JM, Grisoni F, Grulke CM, Hartung T, Hirn M, Karpov P, Korotcov A, Lavado GJ, Lawless M, Li X, Luechtefeld T, Lunghini F, Mangiatordi GF, Marcou G, Marsh D, Martin T, Mauri A, Muratov EN, Myatt GJ, Nguyen DT, Nicolotti O, Note R, Pande P, Parks AK, Peryea T, Polash AH, Rallo R, Roncaglioni A, Rowlands C, Ruiz P, Russo DP, Sayed A, Sayre R, Sheils T, Siegel C, Silva AC, Simeonov A, Sosnin S, Southall N, Strickland J, Tang Y, Teppen B, Tetko IV, Thomas D, Tkachenko V, Todeschini R, Toma C, Tripodi I, Trisciuzzi D, Tropsha A, Varnek A, Vukovic K, Wang Z, Wang L, Waters KM, Wedlake AJ, Wijeyesakere SJ, Wilson D, Xiao Z, Yang H, Zahoranszky-Kohalmi G, Zakharov AV, Zhang FF, Zhang Z, Zhao T, Zhu H, Zorn KM, Casey W, Kleinstreuer NC. CATMoS: Collaborative Acute Toxicity Modeling Suite. Environ Health Perspect 2021; 129:47013. [PMID: 33929906 PMCID: PMC8086800 DOI: 10.1289/ehp8495] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.
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Affiliation(s)
- Kamel Mansouri
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA
| | - Agnes L. Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | | | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Prachi Pradeep
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | | | - Timothy E.H. Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Dave Allen
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | | | - Davide Ballabio
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Shannon Bell
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Sudin Bhattacharya
- Institute for Quantitative Health Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Joyce V. Bastos
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | - Stephen Boyd
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - J.B. Brown
- Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Stephen J. Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Yaroslav Chushak
- Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA
- Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA
| | - Heather Ciallella
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Alex M. Clark
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA
| | - Viviana Consonni
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | | | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Maxim Fedorov
- Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Feng Gao
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Jeffery M. Gearhart
- Aeromedical Research Department, Force Health Protection, USAFSAM, Dayton, Ohio, USA
- Henry M Jackson Foundation for the Advancement of Military Medicine, Dayton, Ohio, USA
| | - Garett Goh
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Jonathan M. Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Francesca Grisoni
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Christopher M. Grulke
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | | | - Matthew Hirn
- Department of Computational Mathematics, Science & Engineering, Department of Mathematics, Michigan State University, East Lansing, Michigan, USA
| | - Pavel Karpov
- Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany
| | | | - Giovanna J. Lavado
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Xinhao Li
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Filippo Lunghini
- Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Gilles Marcou
- Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France
| | - Dan Marsh
- Underwriters Laboratories, Northbrook, Illinois, USA
| | - Todd Martin
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | | | - Eugene N. Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | | | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Reine Note
- L’Oréal Research & Innovation, Aulnay-sous-Bois, France
| | - Paritosh Pande
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Tyler Peryea
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Robert Rallo
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | | | - Patricia Ruiz
- Office of Innovation and Analytics, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Daniel P. Russo
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | - Ahmed Sayed
- Rosettastein Consulting UG, Freising, Germany
| | - Risa Sayre
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education (ORISE) Research Participation Program, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Timothy Sheils
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Charles Siegel
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Arthur C. Silva
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, Brazil
| | - Anton Simeonov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Sergey Sosnin
- Skoltech, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Noel Southall
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Morrisville, North Carolina, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Brian Teppen
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
| | - Igor V. Tetko
- Institute of Structural Biology, Helmholtz Zentrum München (GmbH), Neuherberg, Germany
- BIGCHEM GmbH, Unterschleissheim, Germany
| | - Dennis Thomas
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Roberto Todeschini
- Milano Chemometrics & QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Cosimo Toma
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Ignacio Tripodi
- Computer Science/Interdisciplinary Quantitative Biology, University of Colorado, Boulder, Colorado, USA
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chemoinformatique, URM7140, Université de Strasbourg, Strasbourg, France
| | - Kristijan Vukovic
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Zhongyu Wang
- School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China
| | - Liguo Wang
- School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China
| | | | - Andrew J. Wedlake
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | | | - Dan Wilson
- The Dow Chemical Company, Midland, Michigan, USA
| | - Zijun Xiao
- School of Environmental Sciences and Technology, Dalian University of Technology; Dalian, Liaoning, China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Gergely Zahoranszky-Kohalmi
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Zhen Zhang
- Dow Agrosciences, Indianapolis, Indiana, USA
| | - Tongan Zhao
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Hao Zhu
- Center for Computational and Integrative Biology, Rutgers University, Camden, New Jersey, USA
| | | | - Warren Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA
| | - Nicole C. Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Research Triangle Park, North Carolina, USA
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7
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Abstract
Simplified molecular input line entry system (SMILES)-based deep learning models are slowly emerging as an important research topic in cheminformatics. In this study, we introduce SMILES pair encoding (SPE), a data-driven tokenization algorithm. SPE first learns a vocabulary of high-frequency SMILES substrings from a large chemical dataset (e.g., ChEMBL) and then tokenizes SMILES based on the learned vocabulary for the actual training of deep learning models. SPE augments the widely used atom-level tokenization by adding human-readable and chemically explainable SMILES substrings as tokens. Case studies show that SPE can achieve superior performances on both molecular generation and quantitative structure-activity relationship (QSAR) prediction tasks. In particular, the SPE-based generative models outperformed the atom-level tokenization model in the aspects of novelty, diversity, and ability to resemble the training set distribution. The performance of SPE-based QSAR prediction models were evaluated using 24 benchmark datasets where SPE consistently either did match or outperform atom-level and k-mer tokenization. Therefore, SPE could be a promising tokenization method for SMILES-based deep learning models. An open-source Python package SmilesPE was developed to implement this algorithm and is now freely available at https://github.com/XinhaoLi74/SmilesPE.
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Affiliation(s)
- Xinhao Li
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
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8
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Takeda K, Ikenaka Y, Fourches D, Tanaka KD, Nakayama SMM, Triki D, Li X, Igarashi M, Tanikawa T, Ishizuka M. The VKORC1 ER-luminal loop mutation (Leu76Pro) leads to a significant resistance to warfarin in black rats (Rattus rattus). Pestic Biochem Physiol 2021; 173:104774. [PMID: 33771253 DOI: 10.1016/j.pestbp.2021.104774] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 12/10/2020] [Accepted: 01/04/2021] [Indexed: 06/12/2023]
Abstract
Well-known 4-hydroxycoumarin derivatives, such as warfarin, act as inhibitors of the vitamin K epoxide reductase (VKOR) and are used as anticoagulants. Mutations of the VKOR enzyme can lead to resistance to those compounds. This has been a problem in using them as medicine or rodenticide. Most of these mutations lie in the vicinity of potential warfarin-binding sites within the ER-luminal loop structure (Lys30, Phe55) and the transmembrane helix (Tyr138). However, a VKOR mutation found in Tokyo in warfarin-resistant rats does not follow that pattern (Leu76Pro), and its effect on VKOR function and structure remains unclear. We conducted both in vitro kinetic analyses and in silico docking studies to characterize the VKOR mutant. On the one hand, resistant rats (R-rats) showed a 37.5-fold increased IC50 value to warfarin when compared to susceptible rats (S-rats); on the other hand, R-rats showed a 16.5-fold lower basal VKOR activity (Vmax/Km). Docking calculations exhibited that the mutated VKOR of R-rats has a decreased affinity for warfarin. Molecular dynamics simulations further revealed that VKOR-associated warfarin was more exposed to solvents in R-rats and key interactions between Lys30, Phe55, and warfarin were less favored. This study concludes that a single mutation of VKOR at position 76 leads to a significant resistance to warfarin by modifying the types and numbers of intermolecular interactions between the two.
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Affiliation(s)
- Kazuki Takeda
- Laboratory of Toxicology, Department of Environmental Sciences, Faculty of Veterinary Medicine, Hokkaido University, Kita-18 Nishi-9, Kita-ku, Sapporo 060-0818, Japan
| | - Yoshinori Ikenaka
- Laboratory of Toxicology, Department of Environmental Sciences, Faculty of Veterinary Medicine, Hokkaido University, Kita-18 Nishi-9, Kita-ku, Sapporo 060-0818, Japan; Water Research Group, Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Kazuyuki D Tanaka
- Technical Research Laboratory, IKARI SHODOKU CO., Ltd., Narashino, Chiba, Japan
| | - Shouta M M Nakayama
- Laboratory of Toxicology, Department of Environmental Sciences, Faculty of Veterinary Medicine, Hokkaido University, Kita-18 Nishi-9, Kita-ku, Sapporo 060-0818, Japan
| | - Dhoha Triki
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Xinhao Li
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Manabu Igarashi
- Division of Global Epidemiology, Research Center for Zoonosis Control, Hokkaido University, Sapporo, Japan; Global Station for Zoonosis Control, Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo, Japan
| | - Tsutomu Tanikawa
- Technical Research Laboratory, IKARI SHODOKU CO., Ltd., Narashino, Chiba, Japan
| | - Mayumi Ishizuka
- Laboratory of Toxicology, Department of Environmental Sciences, Faculty of Veterinary Medicine, Hokkaido University, Kita-18 Nishi-9, Kita-ku, Sapporo 060-0818, Japan.
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9
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Abstract
Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.
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Affiliation(s)
- Brian J Reich
- Department of Statistics, North Carolina State University
| | - Yawen Guan
- Department of Statistics, University of Nebraska
| | - Denis Fourches
- Department of Chemistry, North Carolina State University
| | | | | | - Howard H Chang
- Department of Biostatistics and Bioinformatics, Emory University
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10
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Odenkirk MT, Stratton KG, Gritsenko MA, Bramer LM, Webb-Robertson BJM, Bloodsworth KJ, Weitz KK, Lipton AK, Monroe ME, Ash JR, Fourches D, Taylor BD, Burnum-Johnson KE, Baker ES. Unveiling molecular signatures of preeclampsia and gestational diabetes mellitus with multi-omics and innovative cheminformatics visualization tools. Mol Omics 2020; 16:521-532. [PMID: 32966491 PMCID: PMC7736332 DOI: 10.1039/d0mo00074d] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
To fully enable the development of diagnostic tools and progressive pharmaceutical drugs, it is imperative to understand the molecular changes occurring before and during disease onset and progression. Systems biology assessments utilizing multi-omic analyses (e.g. the combination of proteomics, lipidomics, genomics, etc.) have shown enormous value in determining molecules prevalent in diseases and their associated mechanisms. Herein, we utilized multi-omic evaluations, multi-dimensional analysis methods, and new cheminformatics-based visualization tools to provide an in depth understanding of the molecular changes taking place in preeclampsia (PRE) and gestational diabetes mellitus (GDM) patients. Since PRE and GDM are two prevalent pregnancy complications that result in adverse health effects for both the mother and fetus during pregnancy and later in life, a better understanding of each is essential. The multi-omic evaluations performed here provide new insight into the end-stage molecular profiles of each disease, thereby supplying information potentially crucial for earlier diagnosis and treatments.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, NC 27695, USA.
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11
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Borrel A, Melander C, Fourches D. Cheminformatics Analysis of Fluoroquinolones and their Inhibition Potency Against Four Pathogens. Mol Inform 2020; 40:e2000215. [PMID: 33252197 DOI: 10.1002/minf.202000215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 11/13/2020] [Indexed: 11/08/2022]
Abstract
Drug-resistant bacteria are a worldwide public health concern. As the prevalence of multi-drug resistant pathogens outpaces the discovery of new antibacterials, it is of importance to explore the structure-activity relationships for series of known bactericides with proven scaffolds. Herein, we assembled a set of 507 fluoroquinolone analogues all experimentally tested for their inhibition potency against four pathogens: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae. We relied on cheminformatics techniques to characterize and cluster them based on their structural similarity and analyzed the structure-activity relationships identified for each cluster of fluoroquinolones. Then, we utilized machine learning techniques to develop and validate predictive QSAR models for computing the inhibition potencies (pMIC) of analogues for each pathogen. These QSAR models afforded reasonable external prediction performances (R2≥0.6, MAE∼0.4). This study confirmed that (i) there are both global and local inter-pathogen concordance regarding the antibacterial potency of fluoroquinolones, (ii) small clusters of fluoroquinolone analogues are characterized by unique patterns of strain selectivity and potency, the latter being potentially useful to design new analogues with enhanced potency and/or selectivity towards a given pathogen, and (iii) robust QSAR models were obtained allowing for future design of new bioactive fluoroquinolones.
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Affiliation(s)
- Alexandre Borrel
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Christian Melander
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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12
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Odenkirk MT, Zin PPK, Ash JR, Reif DM, Fourches D, Baker ES. Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems. Analyst 2020; 145:7197-7209. [PMID: 33094747 PMCID: PMC7695036 DOI: 10.1039/d0an01638a] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Since its inception, the main goal of the lipidomics field has been to characterize lipid species and their respective biological roles. However, difficulties in both full speciation and biological interpretation have rendered these objectives extremely challenging and as a result, limited our understanding of lipid mechanisms and dysregulation. While mass spectrometry-based advancements have significantly increased the ability to identify lipid species, less progress has been made surrounding biological interpretations. We have therefore developed a Structural-based Connectivity and Omic Phenotype Evaluations (SCOPE) cheminformatics toolbox to aid in these evaluations. SCOPE enables the assessment and visualization of two main lipidomic associations: structure/biological connections and metadata linkages either separately or in tandem. To assess structure and biological relationships, SCOPE utilizes key lipid structural moieties such as head group and fatty acyl composition and links them to their respective biological relationships through hierarchical clustering and grouped heatmaps. Metadata arising from phenotypic and environmental factors such as age and diet is then correlated with the lipid structures and/or biological relationships, utilizing Toxicological Prioritization Index (ToxPi) software. Here, SCOPE is demonstrated for various applications from environmental studies to clinical assessments to showcase new biological connections not previously observed with other techniques.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, NC 27695, USA.
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13
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Azhagiya Singam ER, Tachachartvanich P, Fourches D, Soshilov A, Hsieh JCY, La Merrill MA, Smith MT, Durkin KA. Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning. Environ Res 2020; 190:109920. [PMID: 32795691 DOI: 10.1016/j.envres.2020.109920] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 07/04/2020] [Indexed: 06/11/2023]
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew's correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.
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Affiliation(s)
| | | | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Anatoly Soshilov
- Division of Scientific Programs, Pesticide and Environmental Toxicology Branch, Water Toxicology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, USA
| | - Jennifer C Y Hsieh
- Division of Scientific Programs, Reproductive and Cancer Hazard Assessment Branch, Cancer Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, USA
| | - Michele A La Merrill
- Department of Environmental Toxicology, University of California, Davis, CA, USA
| | - Martyn T Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA.
| | - Kathleen A Durkin
- Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, CA, USA.
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14
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Abstract
Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R2 ≥ 0.71. and MAE ≤ 0.85), and the pIC50 data set (n = 306, R2 ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R2 but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design.
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Affiliation(s)
- Phyo Phyo Kyaw Zin
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Alexandre Borrel
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
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15
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Cools F, Triki D, Geerts N, Delputte P, Fourches D, Cos P. In vitro and in vivo Evaluation of in silico Predicted Pneumococcal UDPG:PP Inhibitors. Front Microbiol 2020; 11:1596. [PMID: 32760374 PMCID: PMC7373766 DOI: 10.3389/fmicb.2020.01596] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/18/2020] [Indexed: 11/25/2022] Open
Abstract
Pneumonia, of which Streptococcus pneumoniae is the most common causative agent, is considered one of the three top leading causes of death worldwide. As seen in other bacterial species, antimicrobial resistance is on the rise for this pathogen. Therefore, there is a pressing need for novel antimicrobial strategies to combat these infections. Recently, uridine diphosphate glucose pyrophosphorylase (UDPG:PP) has been put forward as a potential drug target worth investigating. Moreover, earlier research demonstrated that streptococci lacking a functional galU gene (encoding for UDPG:PP) were characterized by significantly reduced in vitro and in vivo virulence. Therefore, in this study we evaluated the anti-virulence activity of potential UDPG:PP inhibitors. They were selected in silico using a tailor-made streptococcal homology model, based on earlier listerial research. While the compounds didn’t affect bacterial growth, nor affected in vitro adhesion to and phagocytosis in macrophages, the amount of polysaccharide capsule was significantly reduced after co-incubation with these inhibitors. Moreover, co-incubation proved to have a positive effect on survival in an in vivo Galleria mellonella larval infection model. Therefore, rather than targeting bacterial survival directly, these compounds proved to have an effect on streptococcal virulence by lowering the amount of polysaccharide and thereby probably boosting recognition of this pathogen by the innate immune system. While the compounds need adaptation to broaden their activity to more streptococcal strains rather than being strain-specific, this study consolidates UDPG:PP as a potential novel drug target.
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Affiliation(s)
- Freya Cools
- Department of Pharmaceutical Sciences, Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, Antwerp, Belgium
| | - Dhoha Triki
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States
| | - Nele Geerts
- Department of Pharmaceutical Sciences, Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, Antwerp, Belgium
| | - Peter Delputte
- Department of Pharmaceutical Sciences, Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, Antwerp, Belgium
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States
| | - Paul Cos
- Department of Pharmaceutical Sciences, Laboratory for Microbiology, Parasitology and Hygiene (LMPH), University of Antwerp, Antwerp, Belgium
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16
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 299] [Impact Index Per Article: 74.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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17
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Li X, Fourches D. Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT. J Cheminform 2020; 12:27. [PMID: 33430978 PMCID: PMC7178569 DOI: 10.1186/s13321-020-00430-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 04/15/2020] [Indexed: 12/25/2022] Open
Abstract
Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user’s particular series of compounds. In this study, we propose the Molecular Prediction Model Fine-Tuning (MolPMoFiT) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood–brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far.![]()
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Affiliation(s)
- Xinhao Li
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA.
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18
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Zin PPK, Williams G, Fourches D. SIME: synthetic insight-based macrolide enumerator to generate the V1B library of 1 billion macrolides. J Cheminform 2020; 12:23. [PMID: 33431002 PMCID: PMC7146965 DOI: 10.1186/s13321-020-00427-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 03/27/2020] [Indexed: 11/24/2022] Open
Abstract
We report on a new cheminformatics enumeration technology—SIME, synthetic insight-based macrolide enumerator—a new and improved software technology. SIME can enumerate fully assembled macrolides with synthetic feasibility by utilizing the constitutional and structural knowledge extracted from biosynthetic aspects of macrolides. Taken into account by the software are key information such as positions in macrolide structures at which chemical components can be inserted, and the types of structural motifs and sugars of interest that can be synthesized and incorporated at those positions. Additionally, we report on the chemical distribution analysis of the newly SIME-generated V1B (virtual 1 billion) library of macrolides. Those compounds were built based on the core of the Erythromycin structure, 13 structural motifs and a library of sugars derived from eighteen bioactive macrolides. This new enumeration technology can be coupled with cheminformatics approaches such as QSAR modeling and molecular docking to aid in drug discovery for rational designing of next generation macrolide therapeutics with desirable pharmacokinetic properties.![]()
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Affiliation(s)
- Phyo Phyo Kyaw Zin
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Gavin Williams
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA. .,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. .,Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA.
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19
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Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environ Health Perspect 2020; 128:27002. [PMID: 32074470 DOI: 10.23645/epacomptox.5176876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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20
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Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environ Health Perspect 2020; 128:27002. [PMID: 32074470 PMCID: PMC7064318 DOI: 10.1289/ehp5580] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/27/2019] [Accepted: 12/05/2019] [Indexed: 05/04/2023]
Abstract
BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M. Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G. Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V. Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen – German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B. Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
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Li X, Kleinstreuer NC, Fourches D. Hierarchical Quantitative Structure–Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity. Chem Res Toxicol 2020; 33:353-366. [DOI: 10.1021/acs.chemrestox.9b00259] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Xinhao Li
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Nicole C. Kleinstreuer
- Division of Intramural Research/Biostatistics and Computational Biology Branch, NIEHS, Research Triangle
Park, Durham, North Carolina 27709, United States
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, NIEHS, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
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22
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. Correction: QSAR without borders. Chem Soc Rev 2020; 49:3716. [DOI: 10.1039/d0cs90041a] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Correction for ‘QSAR without borders’ by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.
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23
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Abstract
Motivation Easily navigating chemical space has become more important due to the increasing size and diversity of publicly-accessible databases such as DrugBank, ChEMBL or Tox21. To do so, modelers typically rely on complex projection techniques using molecular descriptors computed for all the chemicals to be visualized. However, the multiple cheminformatics steps required to prepare, characterize, compute and explore those molecules, are technical, typically necessitate scripting skills, and thus represent a real obstacle for non-specialists. Results We developed the ChemMaps.com webserver to easily browse, navigate and mine chemical space. The first version of ChemMaps.com features more than 8000 approved, in development, and rejected drugs, as well as over 47 000 environmental chemicals. Availability and implementation The webserver is freely available at http://www.chemmaps.com.
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Affiliation(s)
- Alexandre Borrel
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.,Division of Intramural Research/Biostatistics and Computational Biology Branch
| | - Nicole C Kleinstreuer
- Division of Intramural Research/Biostatistics and Computational Biology Branch.,National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, NIEHS, RTP, Research Triangle, NC, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
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24
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Abstract
Introduction: Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
| | - Jeremy Ash
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
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25
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Ash JR, Kuenemann MA, Rotroff D, Motsinger-Reif A, Fourches D. Cheminformatics approach to exploring and modeling trait-associated metabolite profiles. J Cheminform 2019; 11:43. [PMID: 31236709 PMCID: PMC6591908 DOI: 10.1186/s13321-019-0366-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 06/17/2019] [Indexed: 12/17/2022] Open
Abstract
Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers. ![]()
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Affiliation(s)
- Jeremy R Ash
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Melaine A Kuenemann
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Daniel Rotroff
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Alison Motsinger-Reif
- Department of Statistics, North Carolina State University, Raleigh, NC, USA.,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA. .,Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
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Burnum-Johnson KE, Zheng X, Dodds JN, Ash J, Fourches D, Nicora CD, Wendler JP, Metz TO, Waters KM, Jansson JK, Smith RD, Baker ES. Ion Mobility Spectrometry and the Omics: Distinguishing Isomers, Molecular Classes and Contaminant Ions in Complex Samples. Trends Analyt Chem 2019; 116:292-299. [PMID: 31798197 DOI: 10.1016/j.trac.2019.04.022] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), reducing the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions.
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Affiliation(s)
| | - Xueyun Zheng
- Department of Chemistry, Texas A &M University, College Station, TX
| | - James N Dodds
- Department of Chemistry, NC State University, Raleigh, NC
| | - Jeremy Ash
- Department of Chemistry, NC State University, Raleigh, NC
| | - Denis Fourches
- Department of Chemistry, NC State University, Raleigh, NC
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Jason P Wendler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Katrina M Waters
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Janet K Jansson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Erin S Baker
- Department of Chemistry, NC State University, Raleigh, NC
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Marceau West R, Lu W, Rotroff DM, Kuenemann MA, Chang SM, Wu MC, Wagner MJ, Buse JB, Motsinger-Reif AA, Fourches D, Tzeng JY. Identifying individual risk rare variants using protein structure guided local tests (POINT). PLoS Comput Biol 2019; 15:e1006722. [PMID: 30779729 PMCID: PMC6396946 DOI: 10.1371/journal.pcbi.1006722] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 03/01/2019] [Accepted: 12/17/2018] [Indexed: 01/08/2023] Open
Abstract
Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.
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Affiliation(s)
- Rachel Marceau West
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Wenbin Lu
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Daniel M. Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Melaine A. Kuenemann
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Sheng-Mao Chang
- Department of Statistics, National Cheng-Kung University, Tainan, Taiwan
| | - Michael C. Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Michael J. Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - John B. Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Alison A. Motsinger-Reif
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Denis Fourches
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Jung-Ying Tzeng
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Statistics, National Cheng-Kung University, Tainan, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
- * E-mail:
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Plundrich NJ, Cook BT, Maleki SJ, Fourches D, Lila MA. Binding of peanut allergen Ara h 2 with Vaccinium fruit polyphenols. Food Chem 2019; 284:287-295. [PMID: 30744860 DOI: 10.1016/j.foodchem.2019.01.081] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 01/08/2019] [Accepted: 01/08/2019] [Indexed: 01/30/2023]
Abstract
The potential for 42 different polyphenols found in Vaccinium fruits to bind to peanut allergen Ara h 2 and inhibit IgE binding epitopes was investigated using cheminformatics techniques. Out of 12 predicted binders, delphinidin-3-glucoside, cyanidin-3-glucoside, procyanidin C1, and chlorogenic acid were further evaluated in vitro. Circular dichroism, UV-Vis spectroscopy, and immunoblotting determined their capacity to (i) bind to Ara h 2, (ii) induce protein secondary structural changes, and (iii) inhibit IgE binding epitopes. UV-Vis spectroscopy clearly indicated that procyanidin C1 and chlorogenic acid interacted with Ara h 2, and circular dichroism results suggested that interactions with these polyphenols resulted in changes to Ara h 2 secondary structures. Immunoblotting showed that procyanidin C1 and chlorogenic acid bound to Ara h 2 significantly decreased the IgE binding capacity by 37% and 50%, respectively. These results suggest that certain polyphenols can inhibit IgE recognition of Ara h 2 by obstructing linear IgE epitopes.
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Affiliation(s)
- Nathalie J Plundrich
- Plants for Human Health Institute, Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, North Carolina Research Campus, Kannapolis, NC 28081, USA
| | - Bethany T Cook
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Soheila J Maleki
- United States Department of Agriculture-Agricultural Research Service-Southern Regional Research Center, New Orleans, LA 70124, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Mary Ann Lila
- Plants for Human Health Institute, Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, North Carolina Research Campus, Kannapolis, NC 28081, USA.
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Zin PPK, Williams G, Fourches D. Cheminformatics-based enumeration and analysis of large libraries of macrolide scaffolds. J Cheminform 2018; 10:53. [PMID: 30421084 PMCID: PMC6755550 DOI: 10.1186/s13321-018-0307-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 11/01/2018] [Indexed: 11/10/2022] Open
Abstract
We report on the development of a cheminformatics enumeration technology and the analysis of a resulting large dataset of virtual macrolide scaffolds. Although macrolides have been shown to have valuable biological properties, there is no ready-to-screen virtual library of diverse macrolides in the public domain. Conducting molecular modeling (especially virtual screening) of these complex molecules is highly relevant as the organic synthesis of these compounds, when feasible, typically requires many synthetic steps, and thus dramatically slows the discovery of new bioactive macrolides. Herein, we introduce a cheminformatics approach and associated software that allows for designing and generating libraries of virtual macrocycle/macrolide scaffolds with user-defined constitutional and structural constraints (e.g., types and numbers of structural motifs to be included in the macrocycle, ring size, maximum number of compounds generated). To study the chemical diversity of such generated molecules, we enumerated V1M (Virtual 1 million Macrolide scaffolds) library, each containing twelve common structural motifs. For each macrolide scaffold, we calculated several key properties, such as molecular weight, hydrogen bond donors/acceptors, topological polar surface area. In this study, we discuss (1) the initial concept and current features of our PKS (polyketides) Enumerator software, (2) the chemical diversity and distribution of structural motifs in V1M library, and (3) the unique opportunities for future virtual screening of such enumerated ensembles of macrolides. Importantly, V1M is provided in the Supplementary Material of this paper allowing other researchers to conduct any type of molecular modeling and virtual screening studies. Therefore, this technology for enumerating extremely large libraries of macrolide scaffolds could hold a unique potential in the field of computational chemistry and drug discovery for rational designing of new antibiotics and anti-cancer agents.
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Affiliation(s)
- Phyo Phyo Kyaw Zin
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Gavin Williams
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC, USA.
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Low YS, Alves VM, Fourches D, Sedykh A, Andrade CH, Muratov EN, Rusyn I, Tropsha A. Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships. J Chem Inf Model 2018; 58:2203-2213. [PMID: 30376324 DOI: 10.1021/acs.jcim.8b00450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens-Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.
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Affiliation(s)
- Yen S Low
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , Goias 74605-170 , Brazil
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center , North Carolina State University , Raleigh , North Carolina 27695 , United States
| | - Alexander Sedykh
- Sciome LLC , Research Triangle Park , North Carolina 27709 , United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , Goias 74605-170 , Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa 65000 , Ukraine
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences , Texas A&M University , College Station , Texas 77843 , United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
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Abstract
INTRODUCTION Given that adverse drug effects (ADEs) have led to post-market patient harm and subsequent drug withdrawal, failure of candidate agents in the drug development process, and other negative outcomes, it is essential to attempt to forecast ADEs and other relevant drug-target-effect relationships as early as possible. Current pharmacologic data sources, providing multiple complementary perspectives on the drug-target-effect paradigm, can be integrated to facilitate the inference of relationships between these entities. OBJECTIVE This study aims to identify both existing and unknown relationships between chemicals (C), protein targets (T), and ADEs (E) based on evidence in the literature. MATERIALS AND METHODS Cheminformatics and data mining approaches were employed to integrate and analyze publicly available clinical pharmacology data and literature assertions interrelating drugs, targets, and ADEs. Based on these assertions, a C-T-E relationship knowledge base was developed. Known pairwise relationships between chemicals, targets, and ADEs were collected from several pharmacological and biomedical data sources. These relationships were curated and integrated according to Swanson's paradigm to form C-T-E triangles. Missing C-E edges were then inferred as C-E relationships. RESULTS Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. Several C-E inferences, including testosterone → myocardial infarction, were identified using inferences based on the literature sources published prior to confirmatory case reports. Timestamping approaches confirmed the predictive ability of this inference strategy on a larger scale. CONCLUSIONS The presented workflow, based on free-access databases and an association-based inference scheme, provided novel C-E relationships that have been validated post hoc in case reports. With refinement of prioritization schemes for the generated C-E inferences, this workflow may provide an effective computational method for the early detection of potential drug candidate ADEs that can be followed by targeted experimental investigations.
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Affiliation(s)
- Mary K La
- Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA
| | - Alexander Sedykh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA
- Sciome LLC, 2 Davis Drive, Research Triangle Park, NC, 27709, USA
| | - Denis Fourches
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Pharmacy Lane, Chapel Hill, NC, 27599, USA.
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Borrel A, Fourches D. RealityConvert: a tool for preparing 3D models of biochemical structures for augmented and virtual reality. Bioinformatics 2018; 33:3816-3818. [PMID: 29036294 DOI: 10.1093/bioinformatics/btx485] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 07/28/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation There is a growing interest for the broad use of Augmented Reality (AR) and Virtual Reality (VR) in the fields of bioinformatics and cheminformatics to visualize complex biological and chemical structures. AR and VR technologies allow for stunning and immersive experiences, offering untapped opportunities for both research and education purposes. However, preparing 3D models ready to use for AR and VR is time-consuming and requires a technical expertise that severely limits the development of new contents of potential interest for structural biologists, medicinal chemists, molecular modellers and teachers. Results Herein we present the RealityConvert software tool and associated website, which allow users to easily convert molecular objects to high quality 3D models directly compatible for AR and VR applications. For chemical structures, in addition to the 3D model generation, RealityConvert also generates image trackers, useful to universally call and anchor that particular 3D model when used in AR applications. The ultimate goal of RealityConvert is to facilitate and boost the development and accessibility of AR and VR contents for bioinformatics and cheminformatics applications. Availability and implementation http://www.realityconvert.com. Contact dfourch@ncsu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexandre Borrel
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
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Mahapatra D, Franzosa JA, Roell K, Kuenemann MA, Houck KA, Reif DM, Fourches D, Kullman SW. Confirmation of high-throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays. Sci Rep 2018; 8:8883. [PMID: 29891985 PMCID: PMC5995905 DOI: 10.1038/s41598-018-27055-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Accepted: 05/30/2018] [Indexed: 01/21/2023] Open
Abstract
High throughput screening (HTS) programs have demonstrated that the Vitamin D receptor (VDR) is activated and/or antagonized by a wide range of structurally diverse chemicals. In this study, we examined the Tox21 qHTS data set generated against VDR for reproducibility and concordance and elucidated functional insights into VDR-xenobiotic interactions. Twenty-one potential VDR agonists and 19 VDR antagonists were identified from a subset of >400 compounds with putative VDR activity and examined for VDR functionality utilizing select orthogonal assays. Transient transactivation assay (TT) using a human VDR plasmid and Cyp24 luciferase reporter construct revealed 20/21 active VDR agonists and 18/19 active VDR antagonists. Mammalian-2-hybrid assay (M2H) was then used to evaluate VDR interactions with co-activators and co-regulators. With the exception of a select few compounds, VDR agonists exhibited significant recruitment of co-regulators and co-activators whereas antagonists exhibited considerable attenuation of recruitment by VDR. A unique set of compounds exhibiting synergistic activity in antagonist mode and no activity in agonist mode was identified. Cheminformatics modeling of VDR-ligand interactions were conducted and revealed selective ligand VDR interaction. Overall, data emphasizes the molecular complexity of ligand-mediated interactions with VDR and suggest that VDR transactivation may be a target site of action for diverse xenobiotics.
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Affiliation(s)
- Debabrata Mahapatra
- Comparative Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA
| | - Jill A Franzosa
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, RTP, Raleigh, North Carolina, USA
| | - Kyle Roell
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Melaine Agnes Kuenemann
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Keith A Houck
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, RTP, Raleigh, North Carolina, USA
| | - David M Reif
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Seth W Kullman
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA. .,Program in Environmental and Molecular Toxicology, North Carolina State University, Raleigh, North Carolina, USA.
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Kuenemann MA, Spears PA, Orndorff PE, Fourches D. In silicoPredicted Glucose-1-phosphate Uridylyltransferase (GalU) Inhibitors Block a Key Pathway Required forListeriaVirulence. Mol Inform 2018. [DOI: 10.1002/minf.201800004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Melaine A. Kuenemann
- Department of Chemistry, Bioinformatics Research Center; North Carolina State University; Raleigh, NC USA
| | - Patricia A. Spears
- Department of Population Health and Pathobiology, College of Veterinary Medicine; North Carolina State University; Raleigh, NC USA
| | - Paul E. Orndorff
- Department of Population Health and Pathobiology, College of Veterinary Medicine; North Carolina State University; Raleigh, NC USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center; North Carolina State University; Raleigh, NC USA
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Van Den Driessche G, Fourches D. Adverse drug reactions triggered by the common HLA-B*57:01 variant: virtual screening of DrugBank using 3D molecular docking. J Cheminform 2018; 10:3. [PMID: 29383457 PMCID: PMC5790764 DOI: 10.1186/s13321-018-0257-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 01/17/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Idiosyncratic adverse drug reactions have been linked to a drug's ability to bind with a human leukocyte antigen (HLA) protein. However, due to the thousands of HLA variants and limited structural data for drug-HLA complexes, predicting a specific drug-HLA combination represents a significant challenge. Recently, we investigated the binding mode of abacavir with the HLA-B*57:01 variant using molecular docking. Herein, we developed a new ensemble screening workflow involving three X-ray crystal derived docking procedures to screen the DrugBank database and identify potentially HLA-B*57:01 liable drugs. Then, we compared our workflow's performance with another model recently developed by Metushi et al., which proposed seven in silico HLA-B*57:01 actives, but were later found to be experimentally inactive. METHODS After curation, there were over 6000 approved and experimental drugs remaining in DrugBank for docking using Schrodinger's GLIDE SP and XP scoring functions. Docking was performed with our new consensus-like ensemble workflow, relying on three different X-ray crystals (3VRI, 3VRJ, and 3UPR) in presence and absence of co-binding peptides. The binding modes of HLA-B*57:01 hit compounds for all three peptides were further explored using 3D interaction fingerprints and hierarchical clustering. RESULTS The screening resulted in 22 hit compounds forecasted to bind HLA-B*57:01 in all docking conditions (SP and XP with and without peptides P1, P2, and P3). These 22 compounds afforded 2D-Tanimoto similarities being less than 0.6 when compared to the structure of native abacavir, whereas their 3D binding mode similarities varied in a broader range (0.2-0.8). Hierarchical clustering using a Ward Linkage revealed different clustering patterns for each co-binding peptide. When we docked Metushi et al.'s seven proposed hits using our workflow, our screening platform identified six out of seven as being inactive. Molecular dynamic simulations were used to explore the stability of abacavir and acyclovir in complex with peptide P3. CONCLUSIONS This study reports on the extensive docking of the DrugBank database and the 22 HLA-B*57:01 liable candidates we identified. Importantly, comparisons between this study and the one by Metushi et al. highlighted new critical and complementary knowledge for the development of future HLA-specific in silico models.
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Affiliation(s)
- George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
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Kuenemann MA, Szymczyk M, Chen Y, Sultana N, Hinks D, Freeman HS, Williams AJ, Fourches D, Vinueza NR. Weaver's historic accessible collection of synthetic dyes: a cheminformatics analysis. Chem Sci 2017; 8:4334-4339. [PMID: 28959395 PMCID: PMC5605791 DOI: 10.1039/c7sc00567a] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 04/05/2017] [Indexed: 11/30/2022] Open
Abstract
We present the Max Weaver Dye Library, a collection of ∼98 000 vials of custom-made and largely sparingly water-soluble dyes. Two years ago, the Eastman Chemical Company donated the library to North Carolina State University. This unique collection of chemicals, housed in the College of Textiles, also includes tens of thousands of fabric samples dyed using some of the library's compounds. Although the collection lies at the core of hundreds of patented inventions, the overwhelming majority of this chemical treasure trove has never been published or shared outside of a small group of scientists. Thus, the goal of this donation was to make this chemical collection, and associated data, available to interested parties in the research community. To date, we have digitized a subset of 2700 dyes which allowed us to start the constitutional and structural analysis of the collection using cheminformatics approaches. Herein, we open the discussion regarding the research opportunities offered by this unique library.
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Affiliation(s)
- Melaine A Kuenemann
- Department of Chemistry , Bioinformatics Research Center , College of Sciences , North Carolina State University , Raleigh , NC 27695 , USA .
| | - Malgorzata Szymczyk
- Department of Textile Engineering , Chemistry and Science , College of Textiles , North Carolina State University , Raleigh , NC 27695 , USA .
| | - Yufei Chen
- Department of Textile Engineering , Chemistry and Science , College of Textiles , North Carolina State University , Raleigh , NC 27695 , USA .
| | - Nadia Sultana
- Department of Textile Engineering , Chemistry and Science , College of Textiles , North Carolina State University , Raleigh , NC 27695 , USA .
| | - David Hinks
- Department of Textile Engineering , Chemistry and Science , College of Textiles , North Carolina State University , Raleigh , NC 27695 , USA .
| | - Harold S Freeman
- Department of Textile Engineering , Chemistry and Science , College of Textiles , North Carolina State University , Raleigh , NC 27695 , USA .
| | - Antony J Williams
- National Center for Computational Toxicology , US EPA , Research Triangle Park , Durham , NC 27711 , USA .
| | - Denis Fourches
- Department of Chemistry , Bioinformatics Research Center , College of Sciences , North Carolina State University , Raleigh , NC 27695 , USA .
| | - Nelson R Vinueza
- Department of Textile Engineering , Chemistry and Science , College of Textiles , North Carolina State University , Raleigh , NC 27695 , USA .
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Ash J, Fourches D. Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories. J Chem Inf Model 2017; 57:1286-1299. [DOI: 10.1021/acs.jcim.7b00048] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Jeremy Ash
- Department of Chemistry,
Bioinformatics Research Center, North Carolina State University, 322 Ricks Hall, 1 Lampe Drive, Raleigh, North Carolina 27695, United States
| | - Denis Fourches
- Department of Chemistry,
Bioinformatics Research Center, North Carolina State University, 322 Ricks Hall, 1 Lampe Drive, Raleigh, North Carolina 27695, United States
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Muratov E, Lewis M, Fourches D, Tropsha A, Cox WC. Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance. Am J Pharm Educ 2017; 81:46. [PMID: 28496266 PMCID: PMC5423062 DOI: 10.5688/ajpe81346] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 04/20/2016] [Indexed: 05/22/2023]
Abstract
Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.
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Affiliation(s)
- Eugene Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Margaret Lewis
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Denis Fourches
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Wendy C. Cox
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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Van Den Driessche G, Fourches D. Adverse drug reactions triggered by the common HLA-B*57:01 variant: a molecular docking study. J Cheminform 2017; 9:13. [PMID: 28303164 PMCID: PMC5337232 DOI: 10.1186/s13321-017-0202-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 02/24/2017] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Human leukocyte antigen (HLA) surface proteins are directly involved in idiosyncratic adverse drug reactions. Herein, we present a structure-based analysis of the common HLA-B*57:01 variant known to be responsible for several HLA-linked adverse effects such as the abacavir hypersensitivity syndrome. METHODS First, we analyzed three X-ray crystal structures involving the HLA-B*57:01 protein variant, the anti-HIV drug abacavir, and different co-binding peptides present in the antigen-binding cleft. We superimposed the three complexes and showed that abacavir had no significant conformational variation whatever the co-binding peptide. Second, we self-docked abacavir in the HLA-B*57:01 antigen binding cleft with and without peptide using Glide. Third, we docked a small test set of 13 drugs with known ADRs and suspected HLA associations. RESULTS In the presence of an endogenous co-binding peptide, we found a significant stabilization (~2 kcal/mol) of the docking scores and identified several modified abacavir-peptide interactions indicating that the peptide does play a role in stabilizing the HLA-abacavir complex. Next, our model was used to dock a test set of 13 drugs at HLA-B*57:01 and measured their predicted binding affinities. Drug-specific interactions were observed at the antigen-binding cleft and we were able to discriminate the compounds with known HLA-B*57:01 liability from inactives. CONCLUSIONS Overall, our study highlights the relevance of molecular docking for evaluating and analyzing complex HLA-drug interactions. This is particularly important for virtual drug screening over thousands of HLA variants as other experimental techniques (e.g., in vitro HTS) and computational approaches (e.g., molecular dynamics) are more time consuming and expensive to conduct. As the attention for drugs' HLA liability is on the rise, we believe this work participates in encouraging the use of molecular modeling for reliably studying and predicting HLA-drug interactions. Graphical abstract.
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Affiliation(s)
- George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC USA
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Alves VM, Capuzzi SJ, Muratov E, Braga RC, Thornton T, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Green Chem 2016; 18:6501-6515. [PMID: 28630595 PMCID: PMC5473635 DOI: 10.1039/c6gc01836j] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.
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Affiliation(s)
- Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Stephen J. Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Rodolpho C. Braga
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Thomas Thornton
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Judy Strickland
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Nicole Kleinstreuer
- National Institutes of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. Green Chem 2016; 18:4348-4360. [PMID: 28503093 PMCID: PMC5423727 DOI: 10.1039/c6gc01492e] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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Affiliation(s)
- Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yen Low
- Netflix, San Francisco, CA 94123, USA
| | - Rodolpho Braga
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elena Mokshyna
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Carolina Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Victor Kuz'min
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Abstract
There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 2016; 124:1023-33. [PMID: 26908244 PMCID: PMC4937869 DOI: 10.1289/ehp.1510267] [Citation(s) in RCA: 214] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 10/05/2015] [Accepted: 02/08/2016] [Indexed: 05/18/2023]
Abstract
BACKGROUND Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. CONCLUSION This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. CITATION Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.
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Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | - Ahmed Abdelaziz
- Institute of Structural Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | | | - Alessandra Roncaglioni
- Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France
| | - Alexey Zakharov
- National Cancer Institute, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, Maryland, USA
| | - Andrew Worth
- Institute for Health and Consumer Protection (IHCP), Joint Research Centre of the European Commission in Ispra, Ispra, Italy
| | - Ann M. Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Christopher M. Grulke
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | | | - Denis Fourches
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dragos Horvath
- Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France
| | - Emilio Benfenati
- Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eva Bay Wedebye
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | | | - Giuseppina M. Incisivo
- Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (USDA), Jefferson, Arizona, USA
| | - Hui W. Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (USDA), Jefferson, Arizona, USA
| | - Igor V. Tetko
- Institute of Structural Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- BigChem GmbH, Neuherberg, Germany
| | - Ilya Balabin
- High Performance Computing, Lockheed Martin, Research Triangle Park, North Carolina, USA
| | - Jayaram Kancherla
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Jie Shen
- Research Institute for Fragrance Materials, Inc., Woodcliff Lake, New Jersey, USA
| | - Julien Burton
- Institute for Health and Consumer Protection (IHCP), Joint Research Centre of the European Commission in Ispra, Ispra, Italy
| | - Marc Nicklaus
- National Cancer Institute, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, Maryland, USA
| | - Matteo Cassotti
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Nikolai G. Nikolov
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | | | - Qingda Zang
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, USDA, Jefferson, Arizona, USA
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Ruili Huang
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sine A. Rosenberg
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Svetoslav Slavov
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina, USA
| | - Xin Hu
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Richard S. Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Address correspondence to R.S. Judson, U.S. EPA, National Center for Computational Toxicology, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA. Telephone: (919) 541-3085. E-mail:
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Fechner U, de Graaf C, Torda AE, Güssregen S, Evers A, Matter H, Hessler G, Richmond NJ, Schmidtke P, Segler MHS, Waller MP, Pleik S, Shea JE, Levine Z, Mullen R, van den Broek K, Epple M, Kuhn H, Truszkowski A, Zielesny A, Fraaije JH, Gracia RS, Kast SM, Bulusu KC, Bender A, Yosipof A, Nahum O, Senderowitz H, Krotzky T, Schulz R, Wolber G, Bietz S, Rarey M, Zimmermann MO, Lange A, Ruff M, Heidrich J, Onlia I, Exner TE, Boeckler FM, Bermudez M, Firaha DS, Hollóczki O, Kirchner B, Tautermann CS, Volkamer A, Eid S, Turk S, Rippmann F, Fulle S, Saleh N, Saladino G, Gervasio FL, Haensele E, Banting L, Whitley DC, Oliveira Santos JSD, Bureau R, Clark T, Sandmann A, Lanig H, Kibies P, Heil J, Hoffgaard F, Frach R, Engel J, Smith S, Basu D, Rauh D, Kohlbacher O, Boeckler FM, Essex JW, Bodnarchuk MS, Ross GA, Finkelmann AR, Göller AH, Schneider G, Husch T, Schütter C, Balducci A, Korth M, Ntie-Kang F, Günther S, Sippl W, Mbaze LM, Ntie-Kang F, Simoben CV, Lifongo LL, Ntie-Kang F, Judson P, Barilla J, Lokajíček MV, Pisaková H, Simr P, Kireeva N, Petrov A, Ostroumov D, Solovev VP, Pervov VS, Friedrich NO, Sommer K, Rarey M, Kirchmair J, Proschak E, Weber J, Moser D, Kalinowski L, Achenbach J, Mackey M, Cheeseright T, Renner G, Renner G, Schmidt TC, Schram J, Egelkraut-Holtus M, van Oeyen A, Kalliokoski T, Fourches D, Ibezim A, Mbah CJ, Adikwu UM, Nwodo NJ, Steudle A, Masek BB, Nagy S, Baker D, Soltanshahi F, Dorfman R, Dubrucq K, Patel H, Koch O, Mrugalla F, Kast SM, Ain QU, Fuchs JE, Owen RM, Omoto K, Torella R, Pryde DC, Glen R, Bender A, Hošek P, Spiwok V, Mervin LH, Barrett I, Firth M, Murray DC, McWilliams L, Cao Q, Engkvist O, Warszycki D, Śmieja M, Bojarski AJ, Aniceto N, Freitas A, Ghafourian T, Herrmann G, Eigner-Pitto V, Naß A, Kurczab R, Bojarski AJ, Lange A, Günther MB, Hennig S, Büttner FM, Schall C, Sievers-Engler A, Ansideri F, Koch P, Stehle T, Laufer S, Böckler FM, Zdrazil B, Montanari F, Ecker GF, Grebner C, Hogner A, Ulander J, Edman K, Guallar V, Tyrchan C, Ulander J, Tyrchan C, Klute W, Bergström F, Kramer C, Nguyen QD, Frach R, Kibies P, Strohfeldt S, Böttcher S, Pongratz T, Horinek D, Kast SM, Rupp B, Al-Yamori R, Lisurek M, Kühne R, Furtado F, van den Broek K, Wessjohann L, Mathea M, Baumann K, Mohamad-Zobir SZ, Fu X, Fan TP, Bender A, Kuhn MA, Sotriffer CA, Zoufir A, Li X, Mervin L, Berg E, Polokoff M, Ihlenfeldt WD, Ihlenfeldt WD, Pretzel J, Alhalabi Z, Fraczkiewicz R, Waldman M, Clark RD, Shaikh N, Garg P, Kos A, Himmler HJ, Sandmann A, Jardin C, Sticht H, Steinbrecher TB, Dahlgren M, Cappel D, Lin T, Wang L, Krilov G, Abel R, Friesner R, Sherman W, Pöhner IA, Panecka J, Wade RC, Bietz S, Schomburg KT, Hilbig M, Rarey M, Jäger C, Wieczorek V, Westerhoff LM, Borbulevych OY, Demuth HU, Buchholz M, Schmidt D, Rickmeyer T, Krotzky T, Kolb P, Mittal S, Sánchez-García E, Nogueira MS, Oliveira TB, da Costa FB, Schmidt TJ. 11th German Conference on Chemoinformatics (GCC 2015) : Fulda, Germany. 8-10 November 2015. J Cheminform 2016; 8:18. [PMID: 29270804 PMCID: PMC4896257 DOI: 10.1186/s13321-016-0119-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Uli Fechner
- GDCh-CIC Division Associated Board Member, Beilstein-Institut zur Förderung der Chemischen Wissenschaften, Trakehner Str. 7-9, 60487, Frankfurt, Germany.
| | - Chris de Graaf
- Division Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University, Amsterdam, The Netherlands
| | - Andrew E Torda
- Centre for Bioinformatics, Uni Hamburg, Bundesstr. 43, 20146, Hamburg, Germany
| | - Stefan Güssregen
- Sanofi-Aventis Deutschland GmbH, 65926, Frankfurt am Main, Germany.
| | - Andreas Evers
- Sanofi-Aventis Deutschland GmbH, 65926, Frankfurt am Main, Germany
| | - Hans Matter
- Sanofi-Aventis Deutschland GmbH, 65926, Frankfurt am Main, Germany
| | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH, 65926, Frankfurt am Main, Germany
| | | | | | - Marwin H S Segler
- Organisch-Chemisches Institut, Westfälische Wilhelms-Universität, Münster, Germany.
| | - Mark P Waller
- Organisch-Chemisches Institut, Westfälische Wilhelms-Universität, Münster, Germany
| | - Stefanie Pleik
- Bundeskriminalamt Wiesbaden, Central Analytics II, 65173, Wiesbaden, Germany
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93111, USA.
| | - Zachary Levine
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93111, USA
| | - Ryan Mullen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, CA, 93111, USA
| | | | - Matthias Epple
- Inorganic Chemistry and Center for Nanointegration, University of Duisburg-Essen, Essen, Germany
| | | | - Andreas Truszkowski
- Inorganic Chemistry and Center for Nanointegration, University of Duisburg-Essen, Essen, Germany.,Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, Recklinghausen, Germany
| | - Achim Zielesny
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, Recklinghausen, Germany.
| | | | | | - Stefan M Kast
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany.
| | - Krishna C Bulusu
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.,Unilever Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK.,Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Abraham Yosipof
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Oren Nahum
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel
| | - Hanoch Senderowitz
- Department of Chemistry, Bar-Ilan University, Ramat-Gan, 5290002, Israel.
| | - Timo Krotzky
- Department of Pharmaceutical Chemistry, University of Marburg, Marburg, Germany.
| | - Robert Schulz
- Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Freie Universität Berlin, Königin-Luise Straße 2+4, 14195, Berlin, Germany. .,Computer-Aided Drug Design, Institute of Pharmacy, Freie Universität Berlin, 14195, Berlin, Germany.
| | - Gerhard Wolber
- Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Freie Universität Berlin, Königin-Luise Straße 2+4, 14195, Berlin, Germany
| | - Stefan Bietz
- Center for Bioinformatics, University of Hamburg, 20146, Hamburg, Germany.
| | - Matthias Rarey
- Center for Bioinformatics, University of Hamburg, 20146, Hamburg, Germany
| | - Markus O Zimmermann
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany.,Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany.,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany.,Departement of Pharmaceutical Science, Mol. Design, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Andreas Lange
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany.,Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany.,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Manuel Ruff
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany.,Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany.,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Johannes Heidrich
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Ionut Onlia
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany.,Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany.,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Thomas E Exner
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany.,Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany.,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Frank M Boeckler
- Department of Pharmaceutical and Medicinal Chemistry, Eberhard Karls University Tuebingen, Tuebingen, Germany.
| | - Marcel Bermudez
- Computer-Aided Drug Design, Institute of Pharmacy, Freie Universität Berlin, 14195, Berlin, Germany.
| | - Dzmitry S Firaha
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115, Bonn, Germany
| | - Oldamur Hollóczki
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115, Bonn, Germany.
| | - Barbara Kirchner
- Mulliken Center for Theoretical Chemistry, University of Bonn, 53115, Bonn, Germany.
| | - Christofer S Tautermann
- Boehringer Ingelheim Pharma GmbH & Co. KG, Lead Identification and Optimization Support, Birkendorfer Str. 65, 88397, Biberach a.d. Riss, Germany
| | - Andrea Volkamer
- BioMed X Innovation Center, Im Neuenheimer Feld 583, 69120, Heidelberg, Germany.
| | - Sameh Eid
- BioMed X Innovation Center, Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Samo Turk
- BioMed X Innovation Center, Im Neuenheimer Feld 583, 69120, Heidelberg, Germany
| | - Friedrich Rippmann
- Merck KGaA, Merck Serono, Global Computational Chemistry, Frankfurter Str. 250, 64293, Darmstadt, Germany
| | - Simone Fulle
- BioMed X Innovation Center, Im Neuenheimer Feld 583, 69120, Heidelberg, Germany.
| | - Noureldin Saleh
- Computer-Chemie-Centrum and Interdisciplinary Center for Molecular Materials Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstraße 25, 91052, Erlangen, Germany
| | - Giorgio Saladino
- Department of Chemistry and Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, United Kingdom
| | - Francesco L Gervasio
- Department of Chemistry and Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, United Kingdom
| | - Elke Haensele
- Centre for Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan Road, Portsmouth, PO1 2DT, United Kingdom
| | - Lee Banting
- Centre for Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan Road, Portsmouth, PO1 2DT, United Kingdom
| | - David C Whitley
- Centre for Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan Road, Portsmouth, PO1 2DT, United Kingdom
| | - Jana Sopkova-de Oliveira Santos
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, UPRES EA 4258 - FR CNRS 3038 INC3M, Boulevard Becquerel, 14032, CAEN Cedex, France
| | - Ronan Bureau
- Centre d'Etudes et de Recherche sur le Médicament de Normandie, UPRES EA 4258 - FR CNRS 3038 INC3M, Boulevard Becquerel, 14032, CAEN Cedex, France
| | - Timothy Clark
- Computer-Chemie-Centrum and Interdisciplinary Center for Molecular Materials Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstraße 25, 91052, Erlangen, Germany.,Centre for Molecular Design, School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan Road, Portsmouth, PO1 2DT, United Kingdom.,Department of Chemistry and Pharmacy, Computer Chemistry Center, FAU Erlangen-Nürnberg, Naegelsbachstr. 25, 91052, Erlangen, Germany
| | - Achim Sandmann
- Bioinformatics, Institute for Biochemistry, FAU Erlangen-Nürnberg, Fahrstr. 17, 91054, Erlangen, Germany.
| | - Harald Lanig
- Central Institute for Scientific Computing (ZISC), FAU-Erlangen-Nürnberg, Martensstr. 5a, 91058, Erlangen, Germany
| | - Patrick Kibies
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany.
| | - Jochen Heil
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany
| | | | - Roland Frach
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany
| | - Julian Engel
- Chemische Biologie, TU Dortmund, 44227, Dortmund, Germany
| | - Steven Smith
- Chemische Biologie, TU Dortmund, 44227, Dortmund, Germany
| | - Debjit Basu
- Chemische Biologie, TU Dortmund, 44227, Dortmund, Germany
| | - Daniel Rauh
- Chemische Biologie, TU Dortmund, 44227, Dortmund, Germany
| | - Oliver Kohlbacher
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany.,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Frank M Boeckler
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard Karls University Tübingen, Tübingen, Germany. .,Center for Bioinformatics Tuebingen (ZBIT), Eberhard Karls University Tuebingen, Tuebingen, Germany.
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
| | | | - Gregory A Ross
- School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.
| | - Arndt R Finkelmann
- Swiss Federal Institute of Technology (ETH), Institute of Pharmaceutical Sciences, 8093, Zürich, Switzerland.
| | - Andreas H Göller
- Bayer Pharma AG, Global Drug Discovery, 42096, Wuppertal, Germany
| | - Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Institute of Pharmaceutical Sciences, 8093, Zürich, Switzerland
| | - Tamara Husch
- Institute for Theoretical Chemistry, Ulm University, 89081, Ulm, Germany
| | - Christoph Schütter
- Helmholtz Institute Ulm, Karlsruhe Institute of Technology, 89081, Ulm, Germany
| | - Andrea Balducci
- Helmholtz Institute Ulm, Karlsruhe Institute of Technology, 89081, Ulm, Germany
| | - Martin Korth
- Institute for Theoretical Chemistry, Ulm University, 89081, Ulm, Germany.
| | - Fidele Ntie-Kang
- Department of Chemistry, University of Buea, Buea, South West Region, Cameroon. .,Institut für Pharmazie, Martin-Luther University of Halle-Wittenberg, Halle, 06120, Germany. .,Department of Chemistry, Chemical and Bioactivity Information Centre, University of Buea, Buea, South West Region, Cameroon.
| | - Stefan Günther
- Institut für Pharmazeutische Wissenschaften, Universität Freiburg, 79104, Freiburg, Germany
| | - Wolfgang Sippl
- Institut für Pharmazie, Martin-Luther University of Halle-Wittenberg, Halle, 06120, Germany.,Institute of Pharmacy, University of Halle, 06120, Halle (Saale), Germany.,Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Halle-Wittenberg, Germany
| | - Luc Meva'a Mbaze
- Department of Chemistry, University of Douala, Douala, Littoral Region, Cameroon
| | - Fidele Ntie-Kang
- Department of Chemistry, University of Buea, Buea, South West Region, Cameroon. .,Institut für Pharmazie, Martin-Luther University of Halle-Wittenberg, Halle, 06120, Germany.
| | - Conrad V Simoben
- Department of Chemistry, Chemical and Bioactivity Information Centre, University of Buea, Buea, South West Region, Cameroon
| | - Lydia L Lifongo
- Department of Chemistry, Chemical and Bioactivity Information Centre, University of Buea, Buea, South West Region, Cameroon
| | - Fidele Ntie-Kang
- Department of Chemistry, University of Buea, Buea, South West Region, Cameroon. .,Institut für Pharmazie, Martin-Luther University of Halle-Wittenberg, Halle, 06120, Germany. .,Chemical and Bioactivity Information Centre, Department of Chemistry, University of Buea, Buea, South West Region, Cameroon.
| | - Philip Judson
- Chemical Bioactivity Information Centre, Heather Lea, Bland Hill, Norwood, Harrogate, HG3 1TE, UK
| | - Jiří Barilla
- Faculty of Science, J. E. Purkinje University in Usti nad Labem, Ústí nad Labem, 400 96, Czech Republic.
| | - Miloš V Lokajíček
- Institute of Physics, Academy of Sciences of the Czech Republic, Praha, 182 21, Czech Republic
| | - Hana Pisaková
- Institute of Physics, Academy of Sciences of the Czech Republic, Praha, 182 21, Czech Republic
| | - Pavel Simr
- Faculty of Science, J. E. Purkinje University in Usti nad Labem, Ústí nad Labem, 400 96, Czech Republic
| | - Natalia Kireeva
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, 119071, Russia. .,Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700, Russia.
| | - Alexandre Petrov
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, 119071, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700, Russia.,Kurnakov Institute of General and Inorganic Chemistry, Moscow, 119071, Russia
| | - Denis Ostroumov
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, 119071, Russia.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia, 141700, Russia
| | - Vitaly P Solovev
- Frumkin Institute of Physical Chemistry and Electrochemistry RAS, Moscow, 119071, Russia
| | - Vladislav S Pervov
- Kurnakov Institute of General and Inorganic Chemistry, Moscow, 119071, Russia
| | - Nils-Ole Friedrich
- University of Hamburg, Center for Bioinformatics, Hamburg, 20146, Germany
| | - Kai Sommer
- University of Hamburg, Center for Bioinformatics, Hamburg, 20146, Germany
| | - Matthias Rarey
- University of Hamburg, Center for Bioinformatics, Hamburg, 20146, Germany
| | - Johannes Kirchmair
- University of Hamburg, Center for Bioinformatics, Hamburg, 20146, Germany.
| | - Eugen Proschak
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt, 60438, Germany.
| | - Julia Weber
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt, 60438, Germany
| | - Daniel Moser
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt, 60438, Germany
| | - Lena Kalinowski
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt, 60438, Germany
| | - Janosch Achenbach
- Institute of Pharmaceutical Chemistry, Goethe University, Frankfurt, 60438, Germany
| | - Mark Mackey
- Cresset, Litlington, Cambridgeshire, SG8 0SS, UK.
| | | | - Gerrit Renner
- Faculty of Chemistry, University of Applied Sciences Niederrhein, Krefeld, 47798, Germany.
| | - Gerrit Renner
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Essen, 45141, Germany
| | - Torsten C Schmidt
- Instrumental Analytical Chemistry, University of Duisburg-Essen, Essen, 45141, Germany
| | - Jürgen Schram
- Faculty of Chemistry, University of Applied Sciences Niederrhein, Krefeld, 47798, Germany
| | | | | | - Tuomo Kalliokoski
- Lead Discovery Center GmbH, Otto-Hahn-Straße 15, 44227, Dortmund, Germany.
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Akachukwu Ibezim
- Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Science, University of Nigeria, Nsukka, 410001, Nigeria
| | - Chika J Mbah
- Department of Pharmaceutics, Faculty of Pharmaceutical Science, University of Nigeria, Nsukka, 410001, Nigeria
| | - Umale M Adikwu
- Department of Pharmaceutics, Faculty of Pharmaceutical Science, University of Nigeria, Nsukka, 410001, Nigeria
| | - Ngozi J Nwodo
- Department of Pharmaceutical and Medicinal Chemistry, Faculty of Pharmaceutical Science, University of Nigeria, Nsukka, 410001, Nigeria.
| | - Alexander Steudle
- Certara International, Martin-Kollar-Straße 17, 81829, München, Germany.
| | | | | | | | | | | | | | - Hitesh Patel
- Department of Chemistry and Chemical Biology, TU Dortmund, Dortmund, Germany.
| | - Oliver Koch
- Department of Chemistry and Chemical Biology, TU Dortmund, Dortmund, Germany.,Department of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Str. 6, 44227, Dortmund, Germany
| | | | - Stefan M Kast
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany.
| | - Qurrat U Ain
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Julian E Fuchs
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Robert M Owen
- Worldwide Medicinal Chemistry, Pfizer Neusentis, The Portway Building, Granta Park, Great Abington, Cambridge, Cb21 6GS, United Kingdom
| | - Kiyoyuki Omoto
- Worldwide Medicinal Chemistry, Pfizer Neusentis, The Portway Building, Granta Park, Great Abington, Cambridge, Cb21 6GS, United Kingdom
| | - Rubben Torella
- Department of Biochemistry, University of Chemistry and Technology, Prague, Technická 3, Prague 6, 166 28, Czech Republic
| | - David C Pryde
- Department of Biochemistry, University of Chemistry and Technology, Prague, Technická 3, Prague 6, 166 28, Czech Republic
| | - Robert Glen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom. .,Unilever Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK. .,Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Petr Hošek
- Department of Biochemistry, University of Chemistry and Technology, Prague, Technická 3, Prague 6, 166 28, Czech Republic
| | - Vojtěch Spiwok
- Department of Biochemistry, University of Chemistry and Technology, Prague, Technická 3, Prague 6, 166 28, Czech Republic.
| | - Lewis H Mervin
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Ian Barrett
- Discovery Sciences, AstraZeneca R&D Cambridge, Cambridge Science Park, UK
| | - Mike Firth
- Discovery Sciences, AstraZeneca R&D Alderley Park, Alderley Park, UK
| | - David C Murray
- Discovery Sciences, AstraZeneca R&D Alderley Park, Alderley Park, UK
| | - Lisa McWilliams
- Discovery Sciences, AstraZeneca R&D Alderley Park, Alderley Park, UK
| | - Qing Cao
- Discovery Sciences, AstraZeneca R&D, Boston, MA, USA
| | - Ola Engkvist
- Chemistry Innovation Centre, AstraZeneca R&D, Mölndal, Sweden
| | - Dawid Warszycki
- Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-343, Poland.
| | - Marek Śmieja
- Faculty of Mathematics and Computer Science Jagiellonian University, Krakow, 30-348, Poland
| | - Andrzej J Bojarski
- Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-343, Poland
| | - Natalia Aniceto
- Medway School of Pharmacy, Universities of Kent and Greenwich, Kent, ME4 4TB, UK
| | - Alex Freitas
- School of Computing, University of Kent, Canterbury, Kent, CT2 7NF, UK
| | - Taravat Ghafourian
- Institute of Pharmacology Polish Academy of Sciences, Krakow, 31-343, Poland.
| | | | | | - Alexandra Naß
- Institut für Pharmazie, Freie Universität Berlin, 14195, Berlin, Deutschland.
| | - Rafał Kurczab
- Department of Medicinal Chemistry, Institute of Pharmacology Polish Academy of Sciences, 12 Smetna Street, 31-343, Cracow, Poland.
| | - Andrzej J Bojarski
- Department of Medicinal Chemistry, Institute of Pharmacology Polish Academy of Sciences, 12 Smetna Street, 31-343, Cracow, Poland
| | - Andreas Lange
- Departement of Pharmaceutical Science, Mol. Design, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany.
| | - Marcel B Günther
- Departement of Pharmaceutical Science, Medicinal Chemistry, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Susanne Hennig
- Departement of Pharmaceutical Science, Mol. Design, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Felix M Büttner
- Interfaculty Institute of Biochemistry, Eberhard Karls University Tuebingen, Hoppe-Seyler-Str. 4, 72076, Tuebingen, Germany
| | - Christoph Schall
- Interfaculty Institute of Biochemistry, Eberhard Karls University Tuebingen, Hoppe-Seyler-Str. 4, 72076, Tuebingen, Germany
| | - Adrian Sievers-Engler
- Departement of Pharmaceutical Analysis and Bioanalysis, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Francesco Ansideri
- Departement of Pharmaceutical Science, Medicinal Chemistry, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Pierre Koch
- Departement of Pharmaceutical Science, Medicinal Chemistry, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Thilo Stehle
- Interfaculty Institute of Biochemistry, Eberhard Karls University Tuebingen, Hoppe-Seyler-Str. 4, 72076, Tuebingen, Germany
| | - Stefan Laufer
- Departement of Pharmaceutical Science, Medicinal Chemistry, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Frank M Böckler
- Departement of Pharmaceutical Science, Mol. Design, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Barbara Zdrazil
- Department of Pharmaceutical Chemistry, Division of Drug Design and Medicinal Chemistry, Pharmacoinformatics Research Group, University of Vienna, Althanstraße 14, 1090, Vienna, Austria.
| | - Floriane Montanari
- Department of Pharmaceutical Chemistry, Division of Drug Design and Medicinal Chemistry, Pharmacoinformatics Research Group, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry, Division of Drug Design and Medicinal Chemistry, Pharmacoinformatics Research Group, University of Vienna, Althanstraße 14, 1090, Vienna, Austria
| | | | | | | | - Karl Edman
- Discovery Sciences, AstraZeneca, Mölndal, Sweden
| | - Victor Guallar
- Joint BSC-IRB Research Program in Computational Biology, BSC, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | | | | | | | | | | | - Christian Kramer
- F. Hoffmann-La Roche, Pharma Early Research and Development, Basel, Switzerland
| | - Quoc Dat Nguyen
- Institute of Pharmacy, University of Halle, 06120, Halle (Saale), Germany.
| | - Roland Frach
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany
| | - Patrick Kibies
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany
| | | | | | - Tim Pongratz
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany
| | - Dominik Horinek
- Institut für Physikalische und Theoretische Chemie, Universität Regensburg, 93040, Regensburg, Germany
| | - Stefan M Kast
- Physikalische Chemie III, TU Dortmund, 44227, Dortmund, Germany.
| | - Bernd Rupp
- Structural Biology, AG Computational Chemistry/Drug Design, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany.
| | - Raed Al-Yamori
- Structural Biology, AG Computational Chemistry/Drug Design, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Michael Lisurek
- Structural Biology, AG Computational Chemistry/Drug Design, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Ronald Kühne
- Structural Biology, AG Computational Chemistry/Drug Design, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Filipe Furtado
- Department of Bioorganic Chemistry, Leibniz-Institute of Plant Biochemistry, Weinberg 3, 06120, Halle (Saale), Germany
| | - Karina van den Broek
- Chemistry Department, Ludwig-Maximilians-Universität München, Butenandtstr. 7, 81377, Munich, Germany
| | - Ludger Wessjohann
- Department of Bioorganic Chemistry, Leibniz-Institute of Plant Biochemistry, Weinberg 3, 06120, Halle (Saale), Germany.
| | - Miriam Mathea
- Institute of Medicinal and Pharmaceutical Chemistry, Braunschweig University of Technology, Braunschweig, Germany.
| | - Knut Baumann
- Institute of Medicinal and Pharmaceutical Chemistry, Braunschweig University of Technology, Braunschweig, Germany
| | - Siti Zuraidah Mohamad-Zobir
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
| | - Xianjun Fu
- School of Information Management, Shandong University of Traditional Chinese Medicine, 250355, Jinan, China
| | - Tai-Ping Fan
- Department of Pharmacology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1PD, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom. .,Unilever Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK. .,Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
| | - Maximilian A Kuhn
- Institute of Pharmacy and Food Chemistry, University of Würzburg, 97074, Würzburg, Germany.
| | - Christoph A Sotriffer
- Institute of Pharmacy and Food Chemistry, University of Würzburg, 97074, Würzburg, Germany
| | - Azedine Zoufir
- Unilever Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Xitong Li
- BioSeek, Inc., 310 Utah 100, South San Francisco, CA, 94080, USA
| | - Lewis Mervin
- Unilever Centre for Molecular Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Ellen Berg
- BioSeek, Inc., 310 Utah 100, South San Francisco, CA, 94080, USA
| | - Mark Polokoff
- BioSeek, Inc., 310 Utah 100, South San Francisco, CA, 94080, USA
| | | | | | - Jette Pretzel
- Department of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Str. 6, 44227, Dortmund, Germany.
| | - Zayan Alhalabi
- Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Halle-Wittenberg, Germany.
| | | | | | | | - Neem Shaikh
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S.A.S. Nagar, Punjab, 160 062, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S.A.S. Nagar, Punjab, 160 062, India.
| | | | | | - Achim Sandmann
- Bioinformatics, Institute for Biochemistry, FAU Erlangen-Nürnberg, Fahrstr. 17, 91054, Erlangen, Germany.
| | - Christophe Jardin
- Bioinformatics, Institute for Biochemistry, FAU Erlangen-Nürnberg, Fahrstr. 17, 91054, Erlangen, Germany
| | - Heinrich Sticht
- Bioinformatics, Institute for Biochemistry, FAU Erlangen-Nürnberg, Fahrstr. 17, 91054, Erlangen, Germany
| | | | - Markus Dahlgren
- Schrödinger Inc., 120 West 45th Street, 17th Floor, New York, NY, 10036, USA
| | - Daniel Cappel
- Schrödinger GmbH, Dynamostr. 13, 68165, Mannheim, Germany
| | - Teng Lin
- Schrödinger Inc., 120 West 45th Street, 17th Floor, New York, NY, 10036, USA
| | - Lingle Wang
- Schrödinger Inc., 120 West 45th Street, 17th Floor, New York, NY, 10036, USA
| | - Goran Krilov
- Schrödinger Inc., 120 West 45th Street, 17th Floor, New York, NY, 10036, USA
| | - Robert Abel
- Schrödinger Inc., 120 West 45th Street, 17th Floor, New York, NY, 10036, USA
| | - Richard Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, New York, NY, 10027, USA
| | - Woody Sherman
- Schrödinger Inc., 120 West 45th Street, 17th Floor, New York, NY, 10036, USA
| | - Ina A Pöhner
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS) gGmbH, Heidelberg, Germany.
| | - Joanna Panecka
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS) gGmbH, Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS) gGmbH, Heidelberg, Germany.,ZMBH-DKFZ Alliance, Center for Molecular Biology, Heidelberg University, Heidelberg, Germany
| | - Stefan Bietz
- Center for Bioinformatics, University of Hamburg, Hamburg, Germany
| | | | - Matthias Hilbig
- Center for Bioinformatics, University of Hamburg, Hamburg, Germany
| | - Matthias Rarey
- Center for Bioinformatics, University of Hamburg, Hamburg, Germany.
| | - Christian Jäger
- Fraunhofer Institute for Cell Therapy and Immunology, Department of Drug Design and Target Validation (IZI-MWT), 06120, Halle (Saale), Germany.
| | - Vivien Wieczorek
- Fraunhofer Institute for Cell Therapy and Immunology, Department of Drug Design and Target Validation (IZI-MWT), 06120, Halle (Saale), Germany
| | - Lance M Westerhoff
- QuantumBio Inc, 2790 West College Avenue, Suite 900, State College, PA, 16801, USA
| | - Oleg Y Borbulevych
- QuantumBio Inc, 2790 West College Avenue, Suite 900, State College, PA, 16801, USA
| | - Hans-Ulrich Demuth
- Fraunhofer Institute for Cell Therapy and Immunology, Department of Drug Design and Target Validation (IZI-MWT), 06120, Halle (Saale), Germany
| | - Mirko Buchholz
- Fraunhofer Institute for Cell Therapy and Immunology, Department of Drug Design and Target Validation (IZI-MWT), 06120, Halle (Saale), Germany
| | - Denis Schmidt
- Pharmaceutical Chemistry, Philipps-University, Marburg, Germany.
| | | | - Timo Krotzky
- Pharmaceutical Chemistry, Philipps-University, Marburg, Germany.,The Cambridge Crystallographic Data Centre, Cambridge, UK
| | - Peter Kolb
- Pharmaceutical Chemistry, Philipps-University, Marburg, Germany
| | - Sumit Mittal
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany.
| | | | - Mauro S Nogueira
- Institute of Pharmaceutical Biology and Phytochemistry, University of Muenster, Correnstraße 48, 48149, Muenster, Germany.
| | - Tiago B Oliveira
- School of Mechanical Engineering, Imperial College London, London, SW1 2AZ, UK
| | - Fernando B da Costa
- School of Mechanical Engineering, Imperial College London, London, SW1 2AZ, UK
| | - Thomas J Schmidt
- Central Institute for Scientific Computing (ZISC), FAU-Erlangen-Nürnberg, Martensstr. 5a, 91058, Erlangen, Germany
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46
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Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 2016; 13:545-56. [PMID: 26669717 DOI: 10.1021/acs.molpharmaceut.5b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.
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Affiliation(s)
- Alexey V Zakharov
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Ekaterina V Varlamova
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.,Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine
| | - Alexey A Lagunin
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.,Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia
| | - Alexander V Dmitriev
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - Victor E Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - Vladimir V Poroikov
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
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47
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Fourches D, Pu D, Li L, Zhou H, Mu Q, Su G, Yan B, Tropsha A. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles. Nanotoxicology 2015; 10:374-83. [PMID: 26525350 DOI: 10.3109/17435390.2015.1073397] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Growing experimental evidences suggest the existence of direct relationships between the surface chemistry of nanomaterials and their biological effects. Herein, we have employed computational approaches to design a set of biologically active carbon nanotubes (CNTs) with controlled protein binding and cytotoxicity. Quantitative structure-activity relationship (QSAR) models were built and validated using a dataset of 83 surface-modified CNTs. A subset of a combinatorial virtual library of 240 000 ligands potentially attachable to CNTs was selected to include molecules that were within the chemical similarity threshold with respect to the modeling set compounds. QSAR models were then employed to virtually screen this subset and prioritize CNTs for chemical synthesis and biological evaluation. Ten putatively active and 10 putatively inactive CNTs decorated with the ligands prioritized by virtual screening for either protein-binding or cytotoxicity assay were synthesized and tested. We found that all 10 putatively inactive and 7 of 10 putatively active CNTs were confirmed in the protein-binding assay, whereas all 10 putatively inactive and 6 of 10 putatively active CNTs were confirmed in the cytotoxicity assay. This proof-of-concept study shows that computational models can be employed to guide the design of surface-modified nanomaterials with the desired biological and safety profiles.
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Affiliation(s)
- Denis Fourches
- a Department of Chemistry , Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
| | - Dongqiuye Pu
- b Laboratory for Molecular Modeling , Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill , NC , USA , and
| | - Liwen Li
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Hongyu Zhou
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Qingxin Mu
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Gaoxing Su
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Bing Yan
- c School of Chemistry and Chemical Engineering, Shandong University , Jinan , P.R. China
| | - Alexander Tropsha
- b Laboratory for Molecular Modeling , Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill , NC , USA , and
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48
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Elkins JM, Fedele V, Szklarz M, Abdul Azeez KR, Salah E, Mikolajczyk J, Romanov S, Sepetov N, Huang XP, Roth BL, Al Haj Zen A, Fourches D, Muratov E, Tropsha A, Morris J, Teicher BA, Kunkel M, Polley E, Lackey KE, Atkinson FL, Overington JP, Bamborough P, Müller S, Price DJ, Willson TM, Drewry DH, Knapp S, Zuercher WJ. Comprehensive characterization of the Published Kinase Inhibitor Set. Nat Biotechnol 2015; 34:95-103. [PMID: 26501955 DOI: 10.1038/nbt.3374] [Citation(s) in RCA: 220] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Accepted: 08/31/2015] [Indexed: 12/21/2022]
Abstract
Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein-coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome.
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Affiliation(s)
- Jonathan M Elkins
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK
| | - Vita Fedele
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK
| | - Marta Szklarz
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK
| | - Kamal R Abdul Azeez
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK
| | - Eidarus Salah
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK
| | | | | | | | - Xi-Ping Huang
- The National Institute of Mental Health Psychoactive Active Drug Screening Program, (NIMH PDSP), Department of Pharmacology and Division of Chemical Biology and Medicinal Chemistry, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Bryan L Roth
- The National Institute of Mental Health Psychoactive Active Drug Screening Program, (NIMH PDSP), Department of Pharmacology and Division of Chemical Biology and Medicinal Chemistry, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Ayman Al Haj Zen
- British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Denis Fourches
- Laboratory for Molecular Modeling Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alex Tropsha
- Laboratory for Molecular Modeling Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Joel Morris
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, Maryland, USA
| | - Beverly A Teicher
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, Maryland, USA
| | - Mark Kunkel
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, Maryland, USA
| | - Eric Polley
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, Maryland, USA
| | - Karen E Lackey
- Medical University of South Carolina, Charleston, South Carolina, USA
| | - Francis L Atkinson
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - John P Overington
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Susanne Müller
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK
| | - Daniel J Price
- Chemical Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA
| | - Timothy M Willson
- Chemical Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA
| | - David H Drewry
- Chemical Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA
| | - Stefan Knapp
- Structural Genomics Consortium and Target Discovery Institute, Nuffield Department of Clinical Medicine, Old Road Campus, University of Oxford, Oxford, UK.,Institute for Pharmaceutical Chemistry, Johann Wolfgang Goethe-University, Frankfurt am Main, Germany.,Buchmann Institute for Molecular Life Sciences (BMLS), Frankfurt am Main, Germany
| | - William J Zuercher
- Chemical Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA
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49
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Low YS, Caster O, Bergvall T, Fourches D, Zang X, Norén GN, Rusyn I, Edwards R, Tropsha A. Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome. J Am Med Inform Assoc 2015; 23:968-78. [PMID: 26499102 PMCID: PMC4997030 DOI: 10.1093/jamia/ocv127] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 07/11/2015] [Indexed: 11/21/2022] Open
Abstract
Objective
Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models.
Materials and Methods
Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan).
Results
We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature.
Discussion
Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts.
Conclusions
We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.
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Affiliation(s)
- Yen S Low
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA Department of Environmental Sciences and Engineering, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Ola Caster
- Uppsala Monitoring Centre, Uppsala, Sweden Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden
| | | | - Denis Fourches
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Xiaoling Zang
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - G Niklas Norén
- Uppsala Monitoring Centre, Uppsala, Sweden Department of Mathematics, Stockholm University, Stockholm, Sweden
| | - Ivan Rusyn
- Department of Environmental Sciences and Engineering, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Alexander Tropsha
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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50
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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