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Matthews N, Kitao A, Laycock S, Hayward S. Haptic-Assisted Interactive Molecular Docking Incorporating Receptor Flexibility. J Chem Inf Model 2019; 59:2900-2912. [DOI: 10.1021/acs.jcim.9b00112] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Nick Matthews
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
| | - Akio Kitao
- School of Life Science and Technology, Tokyo Institute of Technology, 2-12-1 Ookayama, M6-13, Meguro, Tokyo 152-8550, Japan
| | - Stephen Laycock
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
| | - Steven Hayward
- School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
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2
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Knight JL, Krilov G, Borrelli KW, Williams J, Gunn JR, Clowes A, Cheng L, Friesner RA, Abel R. Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods. J Chem Theory Comput 2015; 10:3207-20. [PMID: 26588291 DOI: 10.1021/ct500189s] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.
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Affiliation(s)
- Jennifer L Knight
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - Goran Krilov
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - Kenneth W Borrelli
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - Joshua Williams
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - John R Gunn
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - Alec Clowes
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - Luciano Cheng
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
| | - Richard A Friesner
- Columbia University , Department of Chemistry, 3000 Broadway, MC 3110, New York, New York 10027, United States
| | - Robert Abel
- Schrödinger, 120 West 45th Street, 17th Floor, Tower 45, New York, New York 10036-4041, United States
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3
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Antunes DA, Devaurs D, Kavraki LE. Understanding the challenges of protein flexibility in drug design. Expert Opin Drug Discov 2015; 10:1301-13. [DOI: 10.1517/17460441.2015.1094458] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Fischer M, Coleman RG, Fraser JS, Shoichet BK. Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 2014; 6:575-83. [PMID: 24950326 PMCID: PMC4144196 DOI: 10.1038/nchem.1954] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 04/11/2014] [Indexed: 12/04/2022]
Abstract
Proteins fluctuate between alternative conformations, which presents a challenge for ligand discovery because such flexibility is difficult to treat computationally owing to problems with conformational sampling and energy weighting. Here we describe a flexible docking method that samples and weights protein conformations using experimentally derived conformations as a guide. The crystallographically refined occupancies of these conformations, which are observable in an apo receptor structure, define energy penalties for docking. In a large prospective library screen, we identified new ligands that target specific receptor conformations of a cavity in cytochrome c peroxidase, and we confirm both ligand pose and associated receptor conformation predictions by crystallography. The inclusion of receptor flexibility led to ligands with new chemotypes and physical properties. By exploiting experimental measures of loop and side-chain flexibility, this method can be extended to the discovery of new ligands for hundreds of targets in the Protein Data Bank for which similar experimental information is available.
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Affiliation(s)
- Marcus Fischer
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158
- Faculty of Pharmacy, Donnelly Center, University of Toronto, 160 College St. Toronto Ontario M5S 3E1
| | - Ryan G. Coleman
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94158
- Faculty of Pharmacy, Donnelly Center, University of Toronto, 160 College St. Toronto Ontario M5S 3E1
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Abstract
Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein-ligand applications. We summarise the main topics and recent computational and methodological advances in protein-ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.
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6
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Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit 2013; 26:215-39. [PMID: 23526775 DOI: 10.1002/jmr.2266] [Citation(s) in RCA: 193] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2012] [Revised: 01/16/2013] [Accepted: 01/19/2013] [Indexed: 12/28/2022]
Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences; Monash University; Parkville; VIC; 3052; Australia
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7
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Hu G, Li X, Zhang X, Li Y, Ma L, Yang LM, Liu G, Li W, Huang J, Shen X, Hu L, Zheng YT, Tang Y. Discovery of inhibitors to block interactions of HIV-1 integrase with human LEDGF/p75 via structure-based virtual screening and bioassays. J Med Chem 2012; 55:10108-17. [PMID: 23046280 DOI: 10.1021/jm301226a] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This study aims to identify inhibitors that bind at the interface of HIV-1 integrase (IN) and human LEDGF/p75, which represents a novel target for anti-HIV therapy. To date, only a few such inhibitors have been reported. Here structure-based virtual screening was performed to search for the inhibitors from an in-house library of natural products and their derivatives. Among the 38 compounds selected by our strategy, 18 hits were discovered. The two most potent inhibitors showed IC(50) values at 0.32 and 0.26 μM, respectively. Three compounds were subsequently selected for anti-HIV assays, among which (E)-3-(2-chlorophenyl)-1-(2,4-dihydroxyphenyl)prop-2-en-1-one (NPD170) showed the highest antiviral activity (EC(50) = 1.81 μM). The antiviral mechanism of these compounds was further explored, and the results validated that the compounds interrupted the binding of transfected IN to endogenous LEDGF/p75. These findings could be helpful for anti-HIV drug discovery.
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Affiliation(s)
- Guoping Hu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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8
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Identification of old drugs as potential inhibitors of HIV-1 integrase – human LEDGF/p75 interaction via molecular docking. J Mol Model 2012; 18:4995-5003. [DOI: 10.1007/s00894-012-1494-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2012] [Accepted: 06/05/2012] [Indexed: 01/03/2023]
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Hu G, Kuang G, Xiao W, Li W, Liu G, Tang Y. Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening. J Chem Inf Model 2012; 52:1103-13. [DOI: 10.1021/ci300030u] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Guoping Hu
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guanglin Kuang
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Wen Xiao
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Du J, Sun H, Xi L, Li J, Yang Y, Liu H, Yao X. Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM-GBSA calculation. J Comput Chem 2011; 32:2800-9. [PMID: 21717478 DOI: 10.1002/jcc.21859] [Citation(s) in RCA: 99] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2010] [Revised: 03/29/2011] [Accepted: 05/13/2011] [Indexed: 12/14/2022]
Abstract
Developing chemicals that inhibit checkpoint kinase 1 (Chk1) is a promising adjuvant therapeutic to improve the efficacy and selectivity of DNA-targeting agents. Reliable prediction of binding-free energy and binding affinity of Chk1 inhibitors can provide a guide for rational drug design. In this study, multiple docking strategies and Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) calculation were applied to predict the binding mode and free energy for a series of benzoisoquinolinones as Chk1 inhibitors. Reliable docking results were obtained using induced-fit docking and quantum mechanics/molecular mechanics (QM/MM) docking, which showed superior performance on both ligand binding pose and docking score accuracy to the rigid-receptor docking. Then, the Prime/MM-GBSA method based on the docking complex was used to predict the binding-free energy. The combined use of QM/MM docking and Prime/MM-GBSA method could give a high correlation between the predicted binding-free energy and experimentally determined pIC(50) . The molecular docking combined with Prime/MM-GBSA simulation can not only be used to rapidly and accurately predict the binding-free energy of novel Chk1 inhibitors but also provide a novel strategy for lead discovery and optimization targeting Chk1.
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Affiliation(s)
- Juan Du
- Department of Chemistry, State Key Laboratory of Applied Organic Chemistry, Lanzhou University, Lanzhou 730000, China
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11
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Recent trends and observations in the design of high-quality screening collections. Future Med Chem 2011; 3:751-66. [DOI: 10.4155/fmc.11.15] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.
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12
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Glick M, Jacoby E. The role of computational methods in the identification of bioactive compounds. Curr Opin Chem Biol 2011; 15:540-6. [PMID: 21411361 DOI: 10.1016/j.cbpa.2011.02.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Revised: 02/01/2011] [Accepted: 02/21/2011] [Indexed: 10/18/2022]
Abstract
Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.
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Affiliation(s)
- Meir Glick
- Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
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Brooijmans N, Cross JB, Humblet C. Biased retrieval of chemical series in receptor-based virtual screening. J Comput Aided Mol Des 2010; 24:1053-62. [DOI: 10.1007/s10822-010-9394-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Accepted: 10/19/2010] [Indexed: 11/30/2022]
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14
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Brooijmans N, Humblet C. Chemical Space Sampling in Virtual Screening by Different Crystal Structures. Chem Biol Drug Des 2010; 76:472-9. [DOI: 10.1111/j.1747-0285.2010.01041.x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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