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Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD, Ciriaco F, Amoroso N, Nicolotti O. TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity. Chem Res Toxicol 2024; 37:323-339. [PMID: 38200616 DOI: 10.1021/acs.chemrestox.3c00310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.
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Affiliation(s)
- Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Viviana Giannuzzi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Fedele Bonifazi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Antonella Liantonio
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Annamaria De Luca
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
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2
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Catalytic mechanism for Renilla-type luciferases. Nat Catal 2023. [DOI: 10.1038/s41929-022-00895-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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3
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Dolfus U, Briem H, Rarey M. Visualizing Generic Reaction Patterns. J Chem Inf Model 2022; 62:4680-4689. [PMID: 36169383 DOI: 10.1021/acs.jcim.2c00992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Reaction schemes for organic molecules play a crucial role in modern in silico drug design processes. In contrast to the classical drawn reaction diagrams, computational chemists prefer SMARTS based line notations due to a substantially increased expressiveness and precision. They are used to search databases, calculate synthesizability, generate new molecules, or simulate novel reactions. Working with computer-readable representations of reaction schemes can be challenging due to the complexity of the features to be represented. Line representations of reaction schemes can often be cryptic, even to experienced users. To simplify the work with Reaction SMARTS for synthetic, computational, and medicinal chemists, we introduce a visualization technique for reaction schemes and provide a respective tool, called ReactionViewer. ReactionViewer is able to convert reaction schemes encoded as Reaction SMILES, Reaction SMARTS, or SMIRKS into a visual representation. The visualization technique is based on the concept of structure diagrams and follows IUPAC's "Compendium of Chemical Terminology" definition of chemical reaction equations for the reaction symbols. We demonstrate the applicability of the method using two data sets of organic synthesis reaction schemes taken from recent publications. We discuss various properties of the visualization and highlight its readability and interpretability.
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Affiliation(s)
- Uschi Dolfus
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Hans Briem
- Bayer AG, Research and Development, Pharmaceuticals, Computational Molecular Design Berlin, Building S110, 711, 13342 Berlin, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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4
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Schuffenhauer A, Schneider N, Hintermann S, Auld D, Blank J, Cotesta S, Engeloch C, Fechner N, Gaul C, Giovannoni J, Jansen J, Joslin J, Krastel P, Lounkine E, Manchester J, Monovich LG, Pelliccioli AP, Schwarze M, Shultz MD, Stiefl N, Baeschlin DK. Evolution of Novartis' Small Molecule Screening Deck Design. J Med Chem 2020; 63:14425-14447. [PMID: 33140646 DOI: 10.1021/acs.jmedchem.0c01332] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.
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Affiliation(s)
- Ansgar Schuffenhauer
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Nadine Schneider
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Samuel Hintermann
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Douglas Auld
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jutta Blank
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Simona Cotesta
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Caroline Engeloch
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Nikolas Fechner
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Christoph Gaul
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Jerome Giovannoni
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Johanna Jansen
- Novartis Institutes for BioMedical Research-Emeryville, 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - John Joslin
- Genomics Institute of the Novartis Foundation, San Diego, California 92121, United States
| | - Philipp Krastel
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Eugen Lounkine
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - John Manchester
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Lauren G Monovich
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Anna Paola Pelliccioli
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Manuel Schwarze
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Michael D Shultz
- Novartis Institutes for BioMedical Research Inc., 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Nikolaus Stiefl
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
| | - Daniel K Baeschlin
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002 Basel, Switzerland
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5
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Ehrt C, Krause B, Schmidt R, Ehmki ESR, Rarey M. SMARTS.plus - A Toolbox for Chemical Pattern Design. Mol Inform 2020; 39:e2000216. [PMID: 32997890 PMCID: PMC7757167 DOI: 10.1002/minf.202000216] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 09/28/2020] [Indexed: 11/06/2022]
Abstract
The number of publications concerning Pan-Assay Interference Compounds and related problematic structural motifs in screening libraries is constantly growing. In consequence, filter collections are merged, extended but also critically discussed. Due to the complexity of the chemical pattern language SMARTS, an easy-to-use toolbox enabling every chemist to understand, design and modify chemical patterns is urgently needed. Over the past decade, we developed a series of software tools for visualizing, editing, creating, and analysing chemical patterns. Herein, we highlight how most of these tools can now be easily used as part of the novel SMARTS.plus web server (https://smarts.plus/). As a showcase, we demonstrate how researchers can apply the web server tools within minutes to derive novel SMARTS patterns for the filtering of frequent hitters from their screening libraries with only a little experience with the SMARTS language.
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Affiliation(s)
- Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146, Hamburg, Germany
| | - Bennet Krause
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146, Hamburg, Germany
| | - Robert Schmidt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146, Hamburg, Germany
| | - Emanuel S R Ehmki
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146, Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146, Hamburg, Germany
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6
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Ehmki ESR, Schmidt R, Ohm F, Rarey M. Comparing Molecular Patterns Using the Example of SMARTS: Applications and Filter Collection Analysis. J Chem Inf Model 2019; 59:2572-2586. [DOI: 10.1021/acs.jcim.9b00249] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Robert Schmidt
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Farina Ohm
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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7
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Schmidt R, Ehmki ESR, Ohm F, Ehrlich HC, Mashychev A, Rarey M. Comparing Molecular Patterns Using the Example of SMARTS: Theory and Algorithms. J Chem Inf Model 2019; 59:2560-2571. [DOI: 10.1021/acs.jcim.9b00250] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Robert Schmidt
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | | | - Farina Ohm
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | | | - Andriy Mashychev
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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8
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Shamay Y, Shah J, Işık M, Mizrachi A, Leibold J, Tschaharganeh DF, Roxbury D, Budhathoki-Uprety J, Nawaly K, Sugarman JL, Baut E, Neiman MR, Dacek M, Ganesh KS, Johnson DC, Sridharan R, Chu KL, Rajasekhar VK, Lowe SW, Chodera JD, Heller DA. Quantitative self-assembly prediction yields targeted nanomedicines. NATURE MATERIALS 2018; 17:361-368. [PMID: 29403054 PMCID: PMC5930166 DOI: 10.1038/s41563-017-0007-z] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2016] [Accepted: 12/04/2017] [Indexed: 05/18/2023]
Abstract
Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.
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Affiliation(s)
- Yosi Shamay
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Janki Shah
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mehtap Işık
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Aviram Mizrachi
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Otolaryngology Head and Neck Surgery, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Josef Leibold
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Darjus F Tschaharganeh
- Helmholtz-University Group "Cell Plasticity and Epigenetic Remodeling", German Cancer Research Center (DKFZ) & Institute of Pathology University Hospital, Heidelberg, Germany
| | - Daniel Roxbury
- Department of Chemical Engineering, University of Rhode Island, Kingston, RI, 02881, USA
| | | | - Karla Nawaly
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Emily Baut
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, Cornell University, New York, NY, USA
| | | | - Megan Dacek
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Kripa S Ganesh
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Darren C Johnson
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramya Sridharan
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - Karen L Chu
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, Cornell University, New York, NY, USA
| | | | - Scott W Lowe
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John D Chodera
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel A Heller
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Weill Cornell Medical College, Cornell University, New York, NY, USA.
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9
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From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library. J Biotechnol 2017; 261:207-214. [DOI: 10.1016/j.jbiotec.2017.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 05/31/2017] [Accepted: 06/07/2017] [Indexed: 02/06/2023]
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10
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Zani CL, Carroll AR. Database for Rapid Dereplication of Known Natural Products Using Data from MS and Fast NMR Experiments. JOURNAL OF NATURAL PRODUCTS 2017; 80:1758-1766. [PMID: 28616931 DOI: 10.1021/acs.jnatprod.6b01093] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The discovery of novel and/or new bioactive natural products from biota sources is often confounded by the reisolation of known natural products. Dereplication strategies that involve the analysis of NMR and MS spectroscopic data to infer structural features present in purified natural products in combination with database searches of these substructures provide an efficient method to rapidly identify known natural products. Unfortunately this strategy has been hampered by the lack of publically available and comprehensive natural product databases and open source cheminformatics tools. A new platform, DEREP-NP, has been developed to help solve this problem. DEREP-NP uses the open source cheminformatics program DataWarrior to generate a database containing counts of 65 structural fragments present in 229 358 natural product structures derived from plants, animals, and microorganisms, published before 2013 and freely available in the nonproprietary Universal Natural Products Database (UNPD). By counting the number of times one or more of these structural features occurs in an unknown compound, as deduced from the analysis of its NMR (1H, HSQC, and/or HMBC) and/or MS data, matching structures carrying the same numeric combination of searched structural features can be retrieved from the database. Confirmation that the matching structure is the same compound can then be verified through literature comparison of spectroscopic data. This methodology can be applied to both purified natural products and fractions containing a small number of individual compounds that are often generated as screening libraries. The utility of DEREP-NP has been verified through the analysis of spectra derived from compounds (and fractions containing two or three compounds) isolated from plant, marine invertebrate, and fungal sources. DEREP-NP is freely available at https://github.com/clzani/DEREP-NP and will help to streamline the natural product discovery process.
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Affiliation(s)
- Carlos L Zani
- Natural Products Chemistry Laboratory, Centro de Pesquisa René Rachou-Fiocruz , Belo Horizonte, 30190-002, MG, Brazil
| | - Anthony R Carroll
- Griffith School of Environment, Griffith University , Gold Coast Campus, Southport, QLD 4222, Australia
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11
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Dönertaş HM, Martínez Cuesta S, Rahman SA, Thornton JM. Characterising Complex Enzyme Reaction Data. PLoS One 2016; 11:e0147952. [PMID: 26840640 PMCID: PMC4740462 DOI: 10.1371/journal.pone.0147952] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Accepted: 01/11/2016] [Indexed: 01/05/2023] Open
Abstract
The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g., KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.
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Affiliation(s)
- Handan Melike Dönertaş
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
| | - Sergio Martínez Cuesta
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Syed Asad Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Janet M. Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- * E-mail:
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12
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Bietz S, Schomburg KT, Hilbig M, Rarey M. Discriminative Chemical Patterns: Automatic and Interactive Design. J Chem Inf Model 2015; 55:1535-46. [DOI: 10.1021/acs.jcim.5b00323] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Stefan Bietz
- Center
for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Karen T. Schomburg
- Center
for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Hilbig
- Center
for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Center
for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
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13
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Yang C, Tarkhov A, Marusczyk J, Bienfait B, Gasteiger J, Kleinoeder T, Magdziarz T, Sacher O, Schwab CH, Schwoebel J, Terfloth L, Arvidson K, Richard A, Worth A, Rathman J. New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling. J Chem Inf Model 2015; 55:510-28. [PMID: 25647539 DOI: 10.1021/ci500667v] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.
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Affiliation(s)
- Chihae Yang
- †Molecular Networks GmbH, 91052 Erlangen, Germany.,‡Altamira LLC, Columbus, Ohio 43235, United States.,∥US Food and Drug Administration Center for Food Safety and Applied Nutrition, Office of Food Additive Safety (FDA CFSAN OFAS), College Park, Maryland 20740, United States
| | | | | | | | | | | | | | | | | | | | | | - Kirk Arvidson
- ∥US Food and Drug Administration Center for Food Safety and Applied Nutrition, Office of Food Additive Safety (FDA CFSAN OFAS), College Park, Maryland 20740, United States
| | - Ann Richard
- ⊥National Center for Computational Toxicology, US Environmental Protection Agency (EPA), Research Triangle Park, North Carolina 27711, United States
| | - Andrew Worth
- #EC Joint Research Centre (JRC), I-21027 Ispra, Italy
| | - James Rathman
- ‡Altamira LLC, Columbus, Ohio 43235, United States.,§Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
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14
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Fuenzalida JP, Flores ME, Móniz I, Feijoo M, Goycoolea F, Nishide H, Moreno-Villoslada I. Immobilization of hydrophilic low molecular-weight molecules in nanoparticles of chitosan/poly(sodium 4-styrenesulfonate) assisted by aromatic-aromatic interactions. J Phys Chem B 2014; 118:9782-91. [PMID: 25054833 DOI: 10.1021/jp5037553] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The immobilization of the hydrophilic low molecular-weight cationic molecules rhodamine 6G, methylene blue, and citidine in nanoparticles composed of two opposite charged polyelectrolytes, poly(sodium 4-styrenesulfonate) and chitosan, is studied, and the results correlated with their physicochemical properties. Nanoparticles containing both polyelectrolytes have been synthesized showing hydrodynamic diameters of around 200 nm and tunable zeta potential. It was found that the strength of binding of the cationic molecules to the polyanion bearing charged aromatic groups poly(sodium 4-styrenesulfonate) by means of short-range aromatic-aromatic interactions increases with their hydrophobicity and polarizability, as seen by (1)H NMR and UV-vis spectroscopies, and diafiltration. Consequently, association efficiencies of 45, 21, and 12% have been found for the three molecules, respectively, revealing the different ability of the molecules to be immobilized in the nanoparticles. These results provide a proof of concept on a new strategy of immobilization of hydrophilic low molecular-weight molecules based on aromatic-aromatic interactions between polyelectrolytes and their aromatic counterions.
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Affiliation(s)
- Juan Pablo Fuenzalida
- IBBP, Westfälische Wilhelms-Universität Münster , Schlossgarten 3, 48149 - Münster, Germany
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