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Shin WH, Kihara D. PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581511. [PMID: 38464318 PMCID: PMC10925112 DOI: 10.1101/2024.02.22.581511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
- Center for Cancer Research, Purdue University, West Lafayette, IN, USA
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2
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Jiang W, Chen J, Zhang P, Zheng N, Ma L, Zhang Y, Zhang H. Repurposing Drugs for Inhibition against ALDH2 via a 2D/3D Ligand-Based Similarity Search and Molecular Simulation. Molecules 2023; 28:7325. [PMID: 37959744 PMCID: PMC10650273 DOI: 10.3390/molecules28217325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/22/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Aldehyde dehydrogenase-2 (ALDH2) is a crucial enzyme participating in intracellular aldehyde metabolism and is acknowledged as a potential therapeutic target for the treatment of alcohol use disorder and other addictive behaviors. Using previously reported ALDH2 inhibitors of Daidzin, CVT-10216, and CHEMBL114083 as reference molecules, here we perform a ligand-based virtual screening of world-approved drugs via 2D/3D similarity search methods, followed by the assessments of molecular docking, toxicity prediction, molecular simulation, and the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis. The 2D molecular fingerprinting of ECFP4 and FCFP4 and 3D molecule-shape-based USRCAT methods show good performances in selecting compounds with a strong binding behavior with ALDH2. Three compounds of Zeaxanthin (q = 0), Troglitazone (q = 0), and Sequinavir (q = +1 e) are singled out as potential inhibitors; Zeaxanthin can only be hit via USRCAT. These drugs displayed a stronger binding strength compared to the reported potent inhibitor CVT-10216. Sarizotan (q = +1 e) and Netarsudil (q = 0/+1 e) displayed a strong binding strength with ALDH2 as well, whereas they displayed a shallow penetration into the substrate-binding tunnel of ALDH2 and could not fully occupy it. This likely left a space for substrate binding, and thus they were not ideal inhibitors. The MM-PBSA results indicate that the selected negatively charged compounds from the similarity search and Vina scoring are thermodynamically unfavorable, mainly due to electrostatic repulsion with the receptor (q = -6 e for ALDH2). The electrostatic attraction with positively charged compounds, however, yielded very strong binding results with ALDH2. These findings reveal a deficiency in the modeling of electrostatic interactions (in particular, between charged moieties) in the virtual screening via the 2D/3D similarity search and molecular docking with the Vina scoring system.
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Affiliation(s)
| | | | | | | | | | | | - Haiyang Zhang
- Department of Biological Science and Engineering, School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing100083, China
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3
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Löffler P, Escher BI, Baduel C, Virta MP, Lai FY. Antimicrobial Transformation Products in the Aquatic Environment: Global Occurrence, Ecotoxicological Risks, and Potential of Antibiotic Resistance. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37335844 DOI: 10.1021/acs.est.2c09854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
The global spread of antimicrobial resistance (AMR) is concerning for the health of humans, animals, and the environment in a One Health perspective. Assessments of AMR and associated environmental hazards mostly focus on antimicrobial parent compounds, while largely overlooking their transformation products (TPs). This review lists antimicrobial TPs identified in surface water environments and examines their potential for AMR promotion, ecological risk, as well as human health and environmental hazards using in silico models. Our review also summarizes the key transformation compartments of TPs, related pathways for TPs reaching surface waters and methodologies for studying the fate of TPs. The 56 antimicrobial TPs covered by the review were prioritized via scoring and ranking of various risk and hazard parameters. Most data on occurrences to date have been reported in Europe, while little is known about antibiotic TPs in Africa, Central and South America, Asia, and Oceania. Occurrence data on antiviral TPs and other antibacterial TPs are even scarcer. We propose evaluation of structural similarity between parent compounds and TPs for TP risk assessment. We predicted a risk of AMR for 13 TPs, especially TPs of tetracyclines and macrolides. We estimated the ecotoxicological effect concentrations of TPs from the experimental effect data of the parent chemical for bacteria, algae and water fleas, scaled by potency differences predicted by quantitative structure-activity relationships (QSARs) for baseline toxicity and a scaling factor for structural similarity. Inclusion of TPs in mixtures with their parent increased the ecological risk quotient over the threshold of one for 7 of the 24 antimicrobials included in this analysis, while only one parent had a risk quotient above one. Thirteen TPs, from which 6 were macrolide TPs, posed a risk to at least one of the three tested species. There were 12/21 TPs identified that are likely to exhibit a similar or higher level of mutagenicity/carcinogenicity, respectively, than their parent compound, with tetracycline TPs often showing increased mutagenicity. Most TPs with increased carcinogenicity belonged to sulfonamides. Most of the TPs were predicted to be mobile but not bioaccumulative, and 14 were predicted to be persistent. The six highest-priority TPs originated from the tetracycline antibiotic family and antivirals. This review, and in particular our ranking of antimicrobial TPs of concern, can support authorities in planning related intervention strategies and source mitigation of antimicrobials toward a sustainable future.
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Affiliation(s)
- Paul Löffler
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala SE-75007, Sweden
| | - Beate I Escher
- Department of Cell Toxicology, Helmholtz Centre for Environmental Research, UZ, 04318 Leipzig, Germany
- Eberhard Karls University Tübingen, Environmental Toxicology, Department of Geosciences, 72076 Tübingen, Germany
| | - Christine Baduel
- Université Grenoble Alpes, IRD, CNRS, Grenoble INP, IGE, 38 050 Grenoble, France
| | - Marko P Virta
- Department of Microbiology, Faculty of Agriculture and Forestry, University of Helsinki, 00014 Helsinki, Finland
- Multidisciplinary Center of Excellence in Antimicrobial Resistance Research, Helsinki 00100, Finland
| | - Foon Yin Lai
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Uppsala SE-75007, Sweden
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4
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Guo B, Zheng H, Jiang H, Li X, Guan N, Zuo Y, Zhang Y, Yang H, Wang X. Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy. Brief Bioinform 2023; 24:6995409. [PMID: 36682005 DOI: 10.1093/bib/bbac628] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/12/2022] [Accepted: 12/25/2022] [Indexed: 01/23/2023] Open
Abstract
Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.
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Affiliation(s)
- Binjie Guo
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zheng
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Haohan Jiang
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Xiaodan Li
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Naiyu Guan
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yanming Zuo
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yicheng Zhang
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Hengfu Yang
- School of Computer Science, Hunan First Normal University, Changsha, 410205 Hunan, China
| | - Xuhua Wang
- Department of Neurobiology and Department of Rehabilitation Medicine, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, 226001 Jiangsu, China
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5
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Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools. Sci Rep 2023; 13:3008. [PMID: 36810894 PMCID: PMC9944886 DOI: 10.1038/s41598-023-29996-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/14/2023] [Indexed: 02/24/2023] Open
Abstract
Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.
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6
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Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches. Front Mol Biosci 2023; 10:1110567. [PMID: 36814641 PMCID: PMC9939524 DOI: 10.3389/fmolb.2023.1110567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.
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Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry Education, Sunchon National University, Suncheon, South Korea,Department of Advanced Components and Materials Engineering, Sunchon National University, Suncheon, South Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Takatsugu Hirokawa
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan,Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States,Department of Computer Science, Purdue University, West Lafayette, IN, United States,Center for Cancer Research, Purdue University, West Lafayette, IN, United States,*Correspondence: Daisuke Kihara,
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7
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Blay V, Gailiunaite S, Lee CY, Chang HY, Hupp T, Houston DR, Chi P. Comparison of ATP-binding pockets and discovery of homologous recombination inhibitors. Bioorg Med Chem 2022; 70:116923. [PMID: 35841829 DOI: 10.1016/j.bmc.2022.116923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/16/2022] [Accepted: 07/06/2022] [Indexed: 11/02/2022]
Abstract
The ATP binding sites of many enzymes are structurally related, which complicates their development as therapeutic targets. In this work, we explore a diverse set of ATPases and compare their ATP binding pockets using different strategies, including direct and indirect structural methods, in search of pockets attractive for drug discovery. We pursue different direct and indirect structural strategies, as well as ligandability assessments to help guide target selection. The analyses indicate human RAD51, an enzyme crucial in homologous recombination, as a promising, tractable target. Inhibition of RAD51 has shown promise in the treatment of certain cancers but more potent inhibitors are needed. Thus, we design compounds computationally against the ATP binding pocket of RAD51 with consideration of multiple criteria, including predicted specificity, drug-likeness, and toxicity. The molecules designed are evaluated experimentally using molecular and cell-based assays. Our results provide two novel hit compounds against RAD51 and illustrate a computational pipeline to design new inhibitors against ATPases.
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Affiliation(s)
- Vincent Blay
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK; Department of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, CA 95064, USA; Institute for Integrative Systems Biology (I2Sysbio), Universitat de València and Spanish Research Council (CSIC), 46980 Valencia, Spain.
| | - Saule Gailiunaite
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK
| | - Chih-Ying Lee
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Hao-Yen Chang
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Ted Hupp
- MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK.
| | - Peter Chi
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan; Institute of Biological Chemistry, Academia Sinica, Taipei 11529, Taiwan
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8
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Lenci E, Trabocchi A. Diversity‐Oriented Synthesis and Chemoinformatics: A Fruitful Synergy towards Better Chemical Libraries. European J Org Chem 2022. [DOI: 10.1002/ejoc.202200575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Elena Lenci
- Universita degli Studi di Firenze Department of Chemistry Via della Lastruccia 1350019Italia 50019 Sesto Fiorentino ITALY
| | - Andrea Trabocchi
- University of Florence: Universita degli Studi di Firenze Department of Chemistry "Ugo Schiff" ITALY
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9
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Kandi V, Vundecode A, Godalwar TR, Dasari S, Vadakedath S, Godishala V. The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice. BORNEO JOURNAL OF PHARMACY 2022. [DOI: 10.33084/bjop.v5i2.3013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.
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10
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Kyrylchuk A, Kravets I, Cherednichenko A, Tararina V, Kapeliukha A, Dudenko D, Protopopov M. Creation of targeted compound libraries based on 3D shape recognition. Mol Divers 2022; 27:939-949. [PMID: 35608807 DOI: 10.1007/s11030-022-10447-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/19/2022] [Indexed: 11/30/2022]
Abstract
In the emerging field of drug discovery, rapid virtual screening methods become extremely valuable, especially when dealing with ultra-large databases of organic small bioactive molecules. In this work, we present a fast, computationally resource-efficient, and simple workflow for screening targeted compound libraries generated from ultra-large virtual chemical space. This workflow aims to find compounds with similar molecular 3D shapes with reference ones, and at the same time to expand chemical diversity and to identify new and potentially active scaffolds. This pipeline ensures the enrichment of the generated libraries with novel chemotypes. Also, it was shown that delicate tailoring of the physicochemical parameters of the search set ensures that all library compounds will possess desired property distributions. A visual inspection has shown that found structures bind to the receptor in the same way as the reference ones. Using our screening workflow, we have created a number of conventional protein-targeted libraries: the GPCRs Targeted Library (531 K compounds) and the Protein Kinases Targeted Library (113 K compounds). The described pipeline and scripts are freely accessible at: https://github.com/ChemSpace-LLC/usrcat_sim .
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Affiliation(s)
- Andrii Kyrylchuk
- Chemspace LLC, Kiev, Ukraine.,Institute of Organic Chemistry, National Academy of Sciences, Kiev, Ukraine
| | - Iryna Kravets
- Chemspace LLC, Kiev, Ukraine.,Taras Shevchenko National University of Kyiv, Kiev, Ukraine
| | - Anton Cherednichenko
- Chemspace LLC, Kiev, Ukraine.,Taras Shevchenko National University of Kyiv, Kiev, Ukraine
| | - Valentyna Tararina
- Chemspace LLC, Kiev, Ukraine.,Taras Shevchenko National University of Kyiv, Kiev, Ukraine
| | - Anna Kapeliukha
- Chemspace LLC, Kiev, Ukraine.,Taras Shevchenko National University of Kyiv, Kiev, Ukraine
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11
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Aderinwale T, Bharadwaj V, Christoffer C, Terashi G, Zhang Z, Jahandideh R, Kagaya Y, Kihara D. Real-time structure search and structure classification for AlphaFold protein models. Commun Biol 2022; 5:316. [PMID: 35383281 PMCID: PMC8983703 DOI: 10.1038/s42003-022-03261-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/11/2022] [Indexed: 11/17/2022] Open
Abstract
Last year saw a breakthrough in protein structure prediction, where the AlphaFold2 method showed a substantial improvement in the modeling accuracy. Following the software release of AlphaFold2, predicted structures by AlphaFold2 for proteins in 21 species were made publicly available via the AlphaFold Database. Here, to facilitate structural analysis and application of AlphaFold2 models, we provide the infrastructure, 3D-AF-Surfer, which allows real-time structure-based search for the AlphaFold2 models. In 3D-AF-Surfer, structures are represented with 3D Zernike descriptors (3DZD), which is a rotationally invariant, mathematical representation of 3D shapes. We developed a neural network that takes 3DZDs of proteins as input and retrieves proteins of the same fold more accurately than direct comparison of 3DZDs. Using 3D-AF-Surfer, we report structure classifications of AlphaFold2 models and discuss the correlation between confidence levels of AlphaFold2 models and intrinsic disordered regions.
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Affiliation(s)
- Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Vijay Bharadwaj
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | | | - Yuki Kagaya
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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12
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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13
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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14
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Grimm M, Liu Y, Yang X, Bu C, Xiao Z, Cao Y. LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening. J Chem Inf Model 2020; 60:6044-6053. [DOI: 10.1021/acs.jcim.9b01210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Maximilian Grimm
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Xiaocong Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Chunya Bu
- College of Biological Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
| | - Zhixiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
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15
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Wang Y, Hu J, Lai J, Li Y, Jin H, Zhang L, Zhang LR, Liu ZM. TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network. J Chem Inf Model 2020; 60:2754-2765. [PMID: 32392062 DOI: 10.1021/acs.jcim.0c00005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is anticipated that the integration of deep learning would also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprints, presenting a new one we termed as the three-dimensional force fields fingerprint (TF3P). TF3P is learned by a deep capsular network whose training is in no need of labeled data sets for specific predictive tasks. TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other 2D or 3D fingerprints based on only ligands similarity. Furthermore, TF3P is compatible with both statistical models (e.g., similarity ensemble approach) and machine learning models. Altogether, we report TF3P as a new 3D small molecule fingerprint with a promising future in ligand-based drug discovery. All codes are written in Python and available at https://github.com/canisw/tf3p.
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Affiliation(s)
- Yanxing Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
| | - Jianxing Hu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
| | - Junyong Lai
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
| | - Yibo Li
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100191, P. R. China
| | - Hongwei Jin
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
| | - Lihe Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
| | - Liang-Ren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
| | - Zhen-Ming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, P. R. China
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16
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Comparing a Query Compound with Drug Target Classes Using 3D-Chemical Similarity. Int J Mol Sci 2020; 21:ijms21124208. [PMID: 32545691 PMCID: PMC7352980 DOI: 10.3390/ijms21124208] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 06/01/2020] [Accepted: 06/11/2020] [Indexed: 12/16/2022] Open
Abstract
3D similarity is useful in predicting the profiles of unprecedented molecular frameworks that are 2D dissimilar to known compounds. When comparing pairs of compounds, 3D similarity of the pairs depends on conformational sampling, the alignment method, the chosen descriptors, and the similarity coefficients. In addition to these four factors, 3D chemocentric target prediction of an unknown compound requires compound-target associations, which replace compound-to-compound comparisons with compound-to-target comparisons. In this study, quantitative comparison of query compounds to target classes (one-to-group) was achieved via two types of 3D similarity distributions for the respective target class with parameter optimization for the fitting models: (1) maximum likelihood (ML) estimation of queries, and (2) the Gaussian mixture model (GMM) of target classes. While Jaccard-Tanimoto similarity of query-to-ligand pairs with 3D structures (sampled multi-conformers) can be transformed into query distribution using ML estimation, the ligand pair similarity within each target class can be transformed into a representative distribution of a target class through GMM, which is hyperparameterized via the expectation-maximization (EM) algorithm. To quantify the discriminativeness of a query ligand against target classes, the Kullback-Leibler (K-L) divergence of each query was calculated and compared between targets. 3D similarity-based K-L divergence together with the probability and the feasibility index, (Fm), showed discriminative power with regard to some query-class associations. The K-L divergence of 3D similarity distributions can be an additional method for (1) the rank of the 3D similarity score or (2) the p-value of one 3D similarity distribution to predict the target of unprecedented drug scaffolds.
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17
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Zhao B, Li W, Sun L, Fu W. The Use of Computational Approaches in the Discovery and Mechanism Study of Opioid Analgesics. Front Chem 2020; 8:335. [PMID: 32500054 PMCID: PMC7242749 DOI: 10.3389/fchem.2020.00335] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 03/31/2020] [Indexed: 12/15/2022] Open
Abstract
Opioid receptors that belong to class A G protein-coupled receptors (GPCRs) are vital in pain control. In the past few years, published high-resolution crystal structures of opioid receptor laid a solid basis for both experimental and computational studies. Computer-aided drug design (CADD) has been established as a powerful tool for discovering novel lead compounds and for understanding activation mechanism of target receptors. Herein, we reviewed the computational-guided studies on opioid receptors for the discovery of new analgesics, the structural basis of receptor subtype selectivity, agonist interaction mechanism, and biased signaling mechanism.
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Affiliation(s)
- Bangyi Zhao
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai, China
| | - Wei Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai, China
| | - Lijie Sun
- Shijiazhuang No. 4 Pharmaceutical Co., Ltd., Shijiazhuang Economic and Technological Development Zone, Shijiazhuang, China
| | - Wei Fu
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai, China
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18
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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19
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Shin WH, Kihara D. Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0. J Comput Aided Mol Des 2019; 33:1083-1094. [PMID: 31506789 DOI: 10.1007/s10822-019-00222-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 08/28/2019] [Indexed: 10/26/2022]
Abstract
Computational prediction of protein-ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand-receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA.,Department of Chemistry Education, Sunchon National University, Suncheon, 57922, Republic of Korea
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA. .,Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. .,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA. .,Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45229, USA.
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20
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Xiong Y, Qiao Y, Kihara D, Zhang HY, Zhu X, Wei DQ. Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates. Curr Drug Metab 2019; 20:229-235. [PMID: 30338736 DOI: 10.2174/1389200219666181019094526] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 08/05/2018] [Accepted: 08/06/2018] [Indexed: 12/23/2022]
Abstract
Background:Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450.Objective:This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates.Results:Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors.Conclusion:This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.
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Affiliation(s)
- Yi Xiong
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanhua Qiao
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN 47907, United States
| | - Hui-Yuan Zhang
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaolei Zhu
- School of Life Sciences, Anhui University, Hefei, Anhui 230601, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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21
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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22
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Brown BP, Mendenhall J, Meiler J. BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping. J Chem Inf Model 2019; 59:689-701. [PMID: 30707580 DOI: 10.1021/acs.jcim.9b00020] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program. BCL::MolAlign accommodates ligand flexibility through a combination of pregenerated conformers and on-the-fly bond rotation. BCL::MolAlign converges on alignment poses by sampling the relative orientations of mutually matching atom pairs between molecules through Monte Carlo Metropolis sampling. Across six diverse ligand data sets, BCL::MolAlign flexible alignment outperforms MOE, ROCS, and FLEXS in recovering native ligand binding poses. Moreover, the BCL::MolAlign alignment score is more predictive of ligand activity than maximum common substructure similarity across 10 data sets. Finally, on a recently published benchmark set of 20 high quality congeneric ligand-protein complexes, BCL::MolAlign is able to recover a larger fraction of native binding poses than maximum common substructure-based alignment and RosettaLigand. BCL::MolAlign can be obtained as part of the Biology and Chemistry Library (BCL) software package freely with an academic license or can be accessed via Web server at http://meilerlab.org/index.php/servers/molalign .
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Affiliation(s)
- Benjamin P Brown
- Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States
| | - Jeffrey Mendenhall
- Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States
| | - Jens Meiler
- Department of Chemistry, Center for Structural Biology , Vanderbilt University , Nashville , Tennessee 37232 , United States.,Departments of Pharmacology and Biomedical Informatics , Vanderbilt University , Nashville , Tennessee 37212 , United States
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23
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A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data. Future Med Chem 2018; 10:2641-2658. [PMID: 30499744 DOI: 10.4155/fmc-2018-0076] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.
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24
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Seddon MP, Cosgrove DA, Packer MJ, Gillet VJ. Alignment-Free Molecular Shape Comparison Using Spectral Geometry: The Framework. J Chem Inf Model 2018; 59:98-116. [DOI: 10.1021/acs.jcim.8b00676] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Matthew P. Seddon
- Information School, University of Sheffield, Regent Court, 211
Portobello, Sheffield S1 4DP, United Kingdom
| | - David A. Cosgrove
- Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Martin J. Packer
- Chemistry, Oncology, IMED Biotech Unit, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Valerie J. Gillet
- Information School, University of Sheffield, Regent Court, 211
Portobello, Sheffield S1 4DP, United Kingdom
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25
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Vázquez J, Deplano A, Herrero A, Ginex T, Gibert E, Rabal O, Oyarzabal J, Herrero E, Luque FJ. Development and Validation of Molecular Overlays Derived from Three-Dimensional Hydrophobic Similarity with PharmScreen. J Chem Inf Model 2018; 58:1596-1609. [PMID: 30010337 DOI: 10.1021/acs.jcim.8b00216] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular alignment is a standard procedure for three-dimensional (3D) similarity measurements and pharmacophore elucidation. This process is influenced by several factors, such as the physicochemical descriptors utilized to account for the molecular determinants of biological activity and the reference templates. Relying on the hypothesis that the maximal achievable binding affinity for a drug-like molecule is largely due to desolvation, we explore a novel strategy for 3D molecular overlays that exploits the partitioning of molecular hydrophobicity into atomic contributions in conjunction with information about the distribution of hydrogen-bond (HB) donor/acceptor groups. A brief description of the method, as implemented in the software package PharmScreen, including the derivation of the fractional hydrophobic contributions within the quantum mechanical version of the Miertus-Scrocco-Tomasi (MST) continuum model, and the procedure utilized for the optimal superposition between molecules, is presented. The computational procedure is calibrated by using a data set of 402 molecules pertaining to 14 distinct targets taken from the literature and validated against the AstraZeneca test, which comprises 121 experimentally derived sets of molecular overlays. The results point out the suitability of the MST-based hydrophobic parameters for generating molecular overlays, as correct predictions were obtained for 94%, 79%, and 54% of the molecules classified into easy, moderate, and hard sets, respectively. Moreover, the results point out that this accuracy is attained at a much lower degree of identity between the templates used by hydrophobic/HB fields and electrostatic/steric ones. These findings support the usefulness of the hydrophobic/HB descriptors to generate complementary overlays that may be valuable to rationalize structure-activity relationships and for virtual screening campaigns.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain.,Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
| | - Alessandro Deplano
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Albert Herrero
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Tiziana Ginex
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
| | - Enric Gibert
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA) , University of Navarra , Avda. Pio XII 55 , Pamplona E-31008 , Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA) , University of Navarra , Avda. Pio XII 55 , Pamplona E-31008 , Spain
| | - Enric Herrero
- Pharmacelera , Plaça Pau Vila, 1, Sector C 2a , Edifici Palau de Mar, Barcelona 08039 , Spain
| | - F Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB) , University of Barcelona , Av. Prat de la Riba 171 , Santa Coloma de Gramenet E-08921 , Spain
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26
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Habgood M. Conformational ensemble comparison for small molecules in drug discovery. J Comput Aided Mol Des 2018; 32:841-852. [PMID: 29987709 DOI: 10.1007/s10822-018-0132-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 07/04/2018] [Indexed: 11/30/2022]
Abstract
Quantification of three-dimensional similarity between small molecules is a fundamental tool of rational drug design. However, there are no widely-adopted scoring approaches for comparing whole conformational ensembles between molecules. Such scores would be desirable for scenarios in which properties of a molecule have been measured (e.g. activity against a target) but the relevant three dimensional structure is not known. In this study, a set of three complementary ensemble comparison scores is proposed. These are the maximum similarity between any pair of conformations; the proportion of the whole set of the conformations that are matched to within a threshold 3D similarity score; and the average value over these matched conformations of the molecular shape descriptor 'σ-fct', introduced by Ballester et al. The utility of this scoring set is demonstrated in three case studies. The first is an attempt to discriminate between the conformational behaviours of a series of compounds with varying types of cyclisations and other conformationally-significant modifications; the second is an analysis of the more and less active members of a series of GPR119 agonists plus an analysis of a series of orexin-1 antagonists; and the third case study is an attempt to obtain enrichment of active against inactive compounds for a subset of the DUD·E dataset, by ensemble comparison against an active reference compound.
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Affiliation(s)
- Matthew Habgood
- Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK.
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27
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Shin WH, Kihara D. Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method. Methods Mol Biol 2018; 1762:105-121. [PMID: 29594770 DOI: 10.1007/978-1-4939-7756-7_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Science, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, West Lafayette, IN, USA. .,Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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Wong LWY, Tam GSS, Chen X, So FTK, Soecipto A, Sheong FK, Sung HHY, Lin Z, Williams ID. A chiral spiroborate anion from diphenyl-l-tartramide [B{l-Tar(NHPh)2}2]−applied to some challenging resolutions. CrystEngComm 2018. [DOI: 10.1039/c8ce00855h] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A chiral spiroborate anion [B{l-Tar(NHPh)2}2]−is effective in challenging high yield, 1-pot resolutions, as for the S-2-phenylpropylammonium salt shown.
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Affiliation(s)
- Lawrence W.-Y. Wong
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Gemma S.-S. Tam
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Xiaoyan Chen
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Frederick T.-K. So
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Aristyo Soecipto
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Fu Kit Sheong
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Herman H.-Y. Sung
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Zhenyang Lin
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
| | - Ian D. Williams
- Department of Chemistry
- Hong Kong University of Science and Technology
- Kowloon
- China
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29
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Llorach-Pares L, Nonell-Canals A, Sanchez-Martinez M, Avila C. Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents. Mar Drugs 2017; 15:E366. [PMID: 29186912 PMCID: PMC5742826 DOI: 10.3390/md15120366] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 11/10/2017] [Accepted: 11/14/2017] [Indexed: 01/12/2023] Open
Abstract
Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium, against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.
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Affiliation(s)
- Laura Llorach-Pares
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), Universitat de Barcelona, 08028 Barcelona, Catalonia, Spain.
- Mind the Byte S.L., 08028 Barcelona, Catalonia, Spain.
| | | | | | - Conxita Avila
- Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IRBio), Universitat de Barcelona, 08028 Barcelona, Catalonia, Spain.
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30
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Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P. DeCAF-Discrimination, Comparison, Alignment Tool for 2D PHarmacophores. Molecules 2017; 22:E1128. [PMID: 28684712 PMCID: PMC6152008 DOI: 10.3390/molecules22071128] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 06/30/2017] [Accepted: 07/04/2017] [Indexed: 01/24/2023] Open
Abstract
Comparison of small molecules is a common component of many cheminformatics workflows, including the design of new compounds and libraries as well as side-effect predictions and drug repurposing. Currently, large-scale comparison methods rely mostly on simple fingerprint representation of molecules, which take into account the structural similarities of compounds. Methods that utilize 3D information depend on multiple conformer generation steps, which are computationally expensive and can greatly influence their results. The aim of this study was to augment molecule representation with spatial and physicochemical properties while simultaneously avoiding conformer generation. To achieve this goal, we describe a molecule as an undirected graph in which the nodes correspond to atoms with pharmacophoric properties and the edges of the graph represent the distances between features. This approach combines the benefits of a conformation-free representation of a molecule with additional spatial information. We implemented our approach as an open-source Python module called DeCAF (Discrimination, Comparison, Alignment tool for 2D PHarmacophores), freely available at http://bitbucket.org/marta-sd/decaf. We show DeCAF's strengths and weaknesses with usage examples and thorough statistical evaluation. Additionally, we show that our method can be manually tweaked to further improve the results for specific tasks. The full dataset on which DeCAF was evaluated and all scripts used to calculate and analyze the results are also provided.
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Affiliation(s)
| | - Piotr Zielenkiewicz
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106 Warsaw, Poland.
- Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland.
| | - Pawel Siedlecki
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106 Warsaw, Poland.
- Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland.
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Abstract
Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.
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Shin WH, Christoffer CW, Wang J, Kihara D. PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation. J Chem Inf Model 2016; 56:1676-91. [PMID: 27500657 PMCID: PMC5037053 DOI: 10.1021/acs.jcim.6b00163] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
| | - Charles W. Christoffer
- Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
| | - Jibo Wang
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, 893 S. Delaware Street, Indianapolis, IN, USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
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Abstract
Chemoinformatics techniques were originally developed for the construction and searching of large archives of chemical structures but they were soon applied to problems in drug discovery and are now playing an increasingly important role in many additional areas of chemistry. This Special Issue contains seven original research articles and four review articles that provide an introduction to several aspects of this rapidly developing field.
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Affiliation(s)
- Peter Willett
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, UK.
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Yang Y, Zhan J, Zhou Y. SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity. J Comput Chem 2016; 37:1734-9. [DOI: 10.1002/jcc.24380] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Revised: 01/12/2016] [Accepted: 03/05/2016] [Indexed: 12/11/2022]
Affiliation(s)
- Yuedong Yang
- Institute for Glycomics and School of Information and Communication TechnologyGriffith UniversityParklands DrSouthport QLD4222 Australia
| | - Jian Zhan
- Institute for Glycomics and School of Information and Communication TechnologyGriffith UniversityParklands DrSouthport QLD4222 Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication TechnologyGriffith UniversityParklands DrSouthport QLD4222 Australia
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Shin WH, Bures MG, Kihara D. PatchSurfers: Two methods for local molecular property-based binding ligand prediction. Methods 2016; 93:41-50. [PMID: 26427548 PMCID: PMC4718779 DOI: 10.1016/j.ymeth.2015.09.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Revised: 09/27/2015] [Accepted: 09/28/2015] [Indexed: 01/09/2023] Open
Abstract
Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided.
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Affiliation(s)
- Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Mark Gregory Bures
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
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36
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Zhu X, Shin WH, Kim H, Kihara D. Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014. J Chem Inf Model 2015; 56:1088-99. [PMID: 26691286 DOI: 10.1021/acs.jcim.5b00625] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.
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Affiliation(s)
- Xiaolei Zhu
- School of Life Science, Anhui University , Hefei, Anhui 230601, China.,Department of Biology Science, Purdue University , West Lafayette, Indiana 47907, United States
| | - Woong-Hee Shin
- Department of Biology Science, Purdue University , West Lafayette, Indiana 47907, United States
| | - Hyungrae Kim
- Department of Biology Science, Purdue University , West Lafayette, Indiana 47907, United States
| | - Daisuke Kihara
- Department of Biology Science, Purdue University , West Lafayette, Indiana 47907, United States.,Department of Computer Science, Purdue University , West Lafayette, Indiana 47907, United States
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