1
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Hwang W, Austin SL, Blondel A, Boittier ED, Boresch S, Buck M, Buckner J, Caflisch A, Chang HT, Cheng X, Choi YK, Chu JW, Crowley MF, Cui Q, Damjanovic A, Deng Y, Devereux M, Ding X, Feig MF, Gao J, Glowacki DR, Gonzales JE, Hamaneh MB, Harder ED, Hayes RL, Huang J, Huang Y, Hudson PS, Im W, Islam SM, Jiang W, Jones MR, Käser S, Kearns FL, Kern NR, Klauda JB, Lazaridis T, Lee J, Lemkul JA, Liu X, Luo Y, MacKerell AD, Major DT, Meuwly M, Nam K, Nilsson L, Ovchinnikov V, Paci E, Park S, Pastor RW, Pittman AR, Post CB, Prasad S, Pu J, Qi Y, Rathinavelan T, Roe DR, Roux B, Rowley CN, Shen J, Simmonett AC, Sodt AJ, Töpfer K, Upadhyay M, van der Vaart A, Vazquez-Salazar LI, Venable RM, Warrensford LC, Woodcock HL, Wu Y, Brooks CL, Brooks BR, Karplus M. CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed. J Phys Chem B 2024; 128:9976-10042. [PMID: 39303207 PMCID: PMC11492285 DOI: 10.1021/acs.jpcb.4c04100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 08/15/2024] [Accepted: 08/22/2024] [Indexed: 09/22/2024]
Abstract
Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.
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Affiliation(s)
- Wonmuk Hwang
- Department
of Biomedical Engineering, Texas A&M
University, College
Station, Texas 77843, United States
- Department
of Materials Science and Engineering, Texas
A&M University, College Station, Texas 77843, United States
- Department
of Physics and Astronomy, Texas A&M
University, College Station, Texas 77843, United States
- Center for
AI and Natural Sciences, Korea Institute
for Advanced Study, Seoul 02455, Republic
of Korea
| | - Steven L. Austin
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Arnaud Blondel
- Institut
Pasteur, Université Paris Cité, CNRS UMR3825, Structural
Bioinformatics Unit, 28 rue du Dr. Roux F-75015 Paris, France
| | - Eric D. Boittier
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Stefan Boresch
- Faculty of
Chemistry, Department of Computational Biological Chemistry, University of Vienna, Wahringerstrasse 17, 1090 Vienna, Austria
| | - Matthias Buck
- Department
of Physiology and Biophysics, Case Western
Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
| | - Joshua Buckner
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Amedeo Caflisch
- Department
of Biochemistry, University of Zürich, CH-8057 Zürich, Switzerland
| | - Hao-Ting Chang
- Institute
of Bioinformatics and Systems Biology, National
Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan, ROC
| | - Xi Cheng
- Shanghai
Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Yeol Kyo Choi
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jhih-Wei Chu
- Institute
of Bioinformatics and Systems Biology, Department of Biological Science
and Technology, Institute of Molecular Medicine and Bioengineering,
and Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung
University, Hsinchu 30010, Taiwan,
ROC
| | - Michael F. Crowley
- Renewable
Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Qiang Cui
- Department
of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department
of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department
of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
| | - Ana Damjanovic
- Department
of Biophysics, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department
of Physics and Astronomy, Johns Hopkins
University, Baltimore, Maryland 21218, United States
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Yuqing Deng
- Shanghai
R&D Center, DP Technology, Ltd., Shanghai 201210, China
| | - Mike Devereux
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Xinqiang Ding
- Department
of Chemistry, Tufts University, Medford, Massachusetts 02155, United States
| | - Michael F. Feig
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
| | - Jiali Gao
- School
of Chemical Biology & Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Institute
of Systems and Physical Biology, Shenzhen
Bay Laboratory, Shenzhen, Guangdong 518055, China
- Department
of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - David R. Glowacki
- CiTIUS
Centro Singular de Investigación en Tecnoloxías Intelixentes
da USC, 15705 Santiago de Compostela, Spain
| | - James E. Gonzales
- Department
of Biomedical Engineering, Texas A&M
University, College
Station, Texas 77843, United States
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Mehdi Bagerhi Hamaneh
- Department
of Physiology and Biophysics, Case Western
Reserve University, School of Medicine, Cleveland, Ohio 44106, United States
| | | | - Ryan L. Hayes
- Department
of Chemical and Biomolecular Engineering, University of California, Irvine, Irvine, California 92697, United States
- Department
of Pharmaceutical Sciences, University of
California, Irvine, Irvine, California 92697, United States
| | - Jing Huang
- Key Laboratory
of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Yandong Huang
- College
of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Phillip S. Hudson
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
- Medicine
Design, Pfizer Inc., Cambridge, Massachusetts 02139, United States
| | - Wonpil Im
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Shahidul M. Islam
- Department
of Chemistry, Delaware State University, Dover, Delaware 19901, United States
| | - Wei Jiang
- Computational
Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Michael R. Jones
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Silvan Käser
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Fiona L. Kearns
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Nathan R. Kern
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Jeffery B. Klauda
- Department
of Chemical and Biomolecular Engineering, Institute for Physical Science
and Technology, Biophysics Program, University
of Maryland, College Park, Maryland 20742, United States
| | - Themis Lazaridis
- Department
of Chemistry, City College of New York, New York, New York 10031, United States
| | - Jinhyuk Lee
- Disease
Target Structure Research Center, Korea
Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
- Department
of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology, Daejeon 34141, Republic of Korea
| | - Justin A. Lemkul
- Department
of Biochemistry, Virginia Polytechnic Institute
and State University, Blacksburg, Virginia 24061, United States
| | - Xiaorong Liu
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Yun Luo
- Department
of Biotechnology and Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, California 91766, United States
| | - Alexander D. MacKerell
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
| | - Markus Meuwly
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
- Department
of Chemistry, Brown University, Providence, Rhode Island 02912, United States
| | - Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Lennart Nilsson
- Karolinska
Institutet, Department of Biosciences and
Nutrition, SE-14183 Huddinge, Sweden
| | - Victor Ovchinnikov
- Harvard
University, Department of Chemistry
and Chemical Biology, Cambridge, Massachusetts 02138, United States
| | - Emanuele Paci
- Dipartimento
di Fisica e Astronomia, Universitá
di Bologna, Bologna 40127, Italy
| | - Soohyung Park
- Department
of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Richard W. Pastor
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Amanda R. Pittman
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Carol Beth Post
- Borch Department
of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Samarjeet Prasad
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Jingzhi Pu
- Department
of Chemistry and Chemical Biology, Indiana
University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Yifei Qi
- School
of Pharmacy, Fudan University, Shanghai 201203, China
| | | | - Daniel R. Roe
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Benoit Roux
- Department
of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | | | - Jana Shen
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Andrew C. Simmonett
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Alexander J. Sodt
- Eunice
Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Kai Töpfer
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Meenu Upadhyay
- Department
of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Arjan van der Vaart
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | | | - Richard M. Venable
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Luke C. Warrensford
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - H. Lee Woodcock
- Department
of Chemistry, University of South Florida, Tampa, Florida 33620, United States
| | - Yujin Wu
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles L. Brooks
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Bernard R. Brooks
- Laboratory
of Computational Biology, National Heart
Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Martin Karplus
- Harvard
University, Department of Chemistry
and Chemical Biology, Cambridge, Massachusetts 02138, United States
- Laboratoire
de Chimie Biophysique, ISIS, Université
de Strasbourg, 67000 Strasbourg, France
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2
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Freidel MR, Vakhariya PA, Sardarni SK, Armen RS. The Dual-Targeted Fusion Inhibitor Clofazimine Binds to the S2 Segment of the SARS-CoV-2 Spike Protein. Viruses 2024; 16:640. [PMID: 38675980 PMCID: PMC11054727 DOI: 10.3390/v16040640] [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: 03/01/2024] [Revised: 03/29/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
Clofazimine and Arbidol have both been reported to be effective in vitro SARS-CoV-2 fusion inhibitors. Both are promising drugs that have been repurposed for the treatment of COVID-19 and have been used in several previous and ongoing clinical trials. Small-molecule bindings to expressed constructs of the trimeric S2 segment of Spike and the full-length SARS-CoV-2 Spike protein were measured using a Surface Plasmon Resonance (SPR) binding assay. We demonstrate that Clofazimine, Toremifene, Arbidol and its derivatives bind to the S2 segment of the Spike protein. Clofazimine provided the most reliable and highest-quality SPR data for binding with S2 over the conditions explored. A molecular docking approach was used to identify the most favorable binding sites on the S2 segment in the prefusion conformation, highlighting two possible small-molecule binding sites for fusion inhibitors. Results related to molecular docking and modeling of the structure-activity relationship (SAR) of a newly reported series of Clofazimine derivatives support the proposed Clofazimine binding site on the S2 segment. When the proposed Clofazimine binding site is superimposed with other experimentally determined coronavirus structures in structure-sequence alignments, the changes in sequence and structure may rationalize the broad-spectrum antiviral activity of Clofazimine in closely related coronaviruses such as SARS-CoV, MERS, hCoV-229E, and hCoV-OC43.
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Affiliation(s)
| | | | | | - Roger S. Armen
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, 901 Walnut St. Suite 918, Philadelphia, PA 19170, USA (P.A.V.); (S.K.S.)
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3
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Menchon G, Maveyraud L, Czaplicki G. Molecular Dynamics as a Tool for Virtual Ligand Screening. Methods Mol Biol 2024; 2714:33-83. [PMID: 37676592 DOI: 10.1007/978-1-0716-3441-7_3] [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] [Indexed: 09/08/2023]
Abstract
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).
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Affiliation(s)
- Grégory Menchon
- Inserm U1242, Oncogenesis, Stress and Signaling (OSS), Université de Rennes 1, Rennes, France
| | - Laurent Maveyraud
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France
| | - Georges Czaplicki
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France.
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4
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Ippolito M, De Pascali F, Hopfinger N, Komolov KE, Laurinavichyute D, Reddy PAN, Sakkal LA, Rajkowski KZ, Nayak AP, Lee J, Lee J, Cao G, Donover PS, Reichman M, An SS, Salvino JM, Penn RB, Armen RS, Scott CP, Benovic JL. Identification of a β-arrestin-biased negative allosteric modulator for the β 2-adrenergic receptor. Proc Natl Acad Sci U S A 2023; 120:e2302668120. [PMID: 37490535 PMCID: PMC10401000 DOI: 10.1073/pnas.2302668120] [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: 02/15/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023] Open
Abstract
Catecholamine-stimulated β2-adrenergic receptor (β2AR) signaling via the canonical Gs-adenylyl cyclase-cAMP-PKA pathway regulates numerous physiological functions, including the therapeutic effects of exogenous β-agonists in the treatment of airway disease. β2AR signaling is tightly regulated by GRKs and β-arrestins, which together promote β2AR desensitization and internalization as well as downstream signaling, often antithetical to the canonical pathway. Thus, the ability to bias β2AR signaling toward the Gs pathway while avoiding β-arrestin-mediated effects may provide a strategy to improve the functional consequences of β2AR activation. Since attempts to develop Gs-biased agonists and allosteric modulators for the β2AR have been largely unsuccessful, here we screened small molecule libraries for allosteric modulators that selectively inhibit β-arrestin recruitment to the receptor. This screen identified several compounds that met this profile, and, of these, a difluorophenyl quinazoline (DFPQ) derivative was found to be a selective negative allosteric modulator of β-arrestin recruitment to the β2AR while having no effect on β2AR coupling to Gs. DFPQ effectively inhibits agonist-promoted phosphorylation and internalization of the β2AR and protects against the functional desensitization of β-agonist mediated regulation in cell and tissue models. The effects of DFPQ were also specific to the β2AR with minimal effects on the β1AR. Modeling, mutagenesis, and medicinal chemistry studies support DFPQ derivatives binding to an intracellular membrane-facing region of the β2AR, including residues within transmembrane domains 3 and 4 and intracellular loop 2. DFPQ thus represents a class of biased allosteric modulators that targets an allosteric site of the β2AR.
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Affiliation(s)
- Michael Ippolito
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Francesco De Pascali
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Nathan Hopfinger
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Konstantin E. Komolov
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Daniela Laurinavichyute
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | | | - Leon A. Sakkal
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, Philadelphia, PA19107
| | - Kyle Z. Rajkowski
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, Philadelphia, PA19107
| | - Ajay P. Nayak
- Center for Translational Medicine, Department of Medicine, and Jane and Leonard Korman Respiratory Institute, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Justin Lee
- Rutgers Institute for Translational Medicine and Science, New Brunswick, NJ08901
| | - Jordan Lee
- Rutgers Institute for Translational Medicine and Science, New Brunswick, NJ08901
| | - Gaoyuan Cao
- Rutgers Institute for Translational Medicine and Science, New Brunswick, NJ08901
| | | | | | - Steven S. An
- Rutgers Institute for Translational Medicine and Science, New Brunswick, NJ08901
- Department of Pharmacology, Rutgers-Robert Wood Johnson Medical School, The State University of New Jersey, Piscataway, NJ08854
| | | | - Raymond B. Penn
- Center for Translational Medicine, Department of Medicine, and Jane and Leonard Korman Respiratory Institute, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Roger S. Armen
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, Philadelphia, PA19107
| | - Charles P. Scott
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
| | - Jeffrey L. Benovic
- Department of Biochemistry and Molecular Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA19107
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5
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Bülbül EF, Melesina J, Ibrahim HS, Abdelsalam M, Vecchio A, Robaa D, Zessin M, Schutkowski M, Sippl W. Docking, Binding Free Energy Calculations and In Vitro Characterization of Pyrazine Linked 2-Aminobenzamides as Novel Class I Histone Deacetylase (HDAC) Inhibitors. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27082526. [PMID: 35458724 PMCID: PMC9032825 DOI: 10.3390/molecules27082526] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/08/2022] [Accepted: 04/09/2022] [Indexed: 11/16/2022]
Abstract
Class I histone deacetylases, HDAC1, HDAC2, and HDAC3, represent potential targets for cancer treatment. However, the development of isoform-selective drugs for these enzymes remains challenging due to their high sequence and structural similarity. In the current study, we applied a computational approach to predict the selectivity profile of developed inhibitors. Molecular docking followed by MD simulation and calculation of binding free energy was performed for a dataset of 2-aminobenzamides comprising 30 previously developed inhibitors. For each HDAC isoform, a significant correlation was found between the binding free energy values and in vitro inhibitory activities. The predictive accuracy and reliability of the best preforming models were assessed on an external test set of newly designed and synthesized inhibitors. The developed binding free-energy models are cost-effective methods and help to reduce the time required to prioritize compounds for further studies.
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Affiliation(s)
- Emre F. Bülbül
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
| | - Jelena Melesina
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
| | - Hany S. Ibrahim
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Egyptian Russian University, Cairo 11829, Egypt
| | - Mohamed Abdelsalam
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Alexandria University, Alexandria 21521, Egypt
| | - Anita Vecchio
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
| | - Dina Robaa
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
| | - Matthes Zessin
- Department of Enzymology, Institute of Biochemistry and Biotechnology, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (M.Z.); (M.S.)
| | - Mike Schutkowski
- Department of Enzymology, Institute of Biochemistry and Biotechnology, Martin-Luther-University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (M.Z.); (M.S.)
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle (Saale), Germany; (E.F.B.); (J.M.); (H.S.I.); (M.A.); (A.V.); (D.R.)
- Correspondence:
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6
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Freidel M, Armen RS. Modeling the Structure-Activity Relationship of Arbidol Derivatives and Other SARS-CoV-2 Fusion Inhibitors Targeting the S2 Segment of the Spike Protein. J Chem Inf Model 2021; 61:5906-5922. [PMID: 34898207 PMCID: PMC8691200 DOI: 10.1021/acs.jcim.1c01061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Umifenovir (Arbidol) has been reported to exhibit some degree of efficacy in multiple clinical trials for the treatment of COVID-19 as a monotherapy. It has also demonstrated synergistic inhibition of SARS-CoV-2 with other direct-acting antivirals such as Remdesivir. A computational approach was used to identify the most favorable binding site to the SARS-CoV-2 Spike S2 segment and to perform virtual screening. Compounds selected from modeling were evaluated in a live SARS-CoV-2 infection assay. An Arbidol (ARB) derivative with substitutions at both the C-4 and C-6 positions was found to exhibit a modest improvement in activity and solubility properties in comparison to ARB. However, all of the derivatives were found to only be partial inhibitors, rather than full inhibitors in a virus-induced cytopathic effect-based assay. The binding mode is also corroborated by parallel modeling of a series of oleanolic acid trisaccharide saponin fusion inhibitors shown to bind to the S2 segment. Recently determined experimental structures of the Spike protein allowed atomic resolution modeling of fusion inhibitor binding as a function of pH, and the implications for the molecular mechanism of direct-acting fusion inhibitors targeting the S2 segment are discussed.
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Affiliation(s)
- Matthew
R. Freidel
- Department of Pharmaceutical
Sciences, College of Pharmacy, Thomas Jefferson
University, 901 Walnut St. Suite 918, Philadelphia, Pennsylvania 19170, United States
| | - Roger S. Armen
- Department of Pharmaceutical
Sciences, College of Pharmacy, Thomas Jefferson
University, 901 Walnut St. Suite 918, Philadelphia, Pennsylvania 19170, United States
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7
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Kamenik AS, Singh I, Lak P, Balius TE, Liedl KR, Shoichet BK. Energy penalties enhance flexible receptor docking in a model cavity. Proc Natl Acad Sci U S A 2021; 118:e2106195118. [PMID: 34475217 PMCID: PMC8433570 DOI: 10.1073/pnas.2106195118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 07/27/2021] [Indexed: 11/18/2022] Open
Abstract
Protein flexibility remains a major challenge in library docking because of difficulties in sampling conformational ensembles with accurate probabilities. Here, we use the model cavity site of T4 lysozyme L99A to test flexible receptor docking with energy penalties from molecular dynamics (MD) simulations. Crystallography with larger and smaller ligands indicates that this cavity can adopt three major conformations: open, intermediate, and closed. Since smaller ligands typically bind better to the cavity site, we anticipate an energy penalty for the cavity opening. To estimate its magnitude, we calculate conformational preferences from MD simulations. We find that including a penalty term is essential for retrospective ligand enrichment; otherwise, high-energy states dominate the docking. We then prospectively docked a library of over 900,000 compounds for new molecules binding to each conformational state. Absent a penalty term, the open conformation dominated the docking results; inclusion of this term led to a balanced sampling of ligands against each state. High ranked molecules were experimentally tested by Tm upshift and X-ray crystallography. From 33 selected molecules, we identified 18 ligands and determined 13 crystal structures. Most interesting were those bound to the open cavity, where the buried site opens to bulk solvent. Here, highly unusual ligands for this cavity had been predicted, including large ligands with polar tails; these were confirmed both by binding and by crystallography. In docking, incorporating protein flexibility with thermodynamic weightings may thus access new ligand chemotypes. The MD approach to accessing and, crucially, weighting such alternative states may find general applicability.
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Affiliation(s)
- Anna S Kamenik
- Institute of General, Inorganic, and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, 6020 Innsbruck, Austria
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Isha Singh
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Parnian Lak
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
| | - Klaus R Liedl
- Institute of General, Inorganic, and Theoretical Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, 6020 Innsbruck, Austria;
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158
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8
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Saikia S, Bordoloi M. Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective. Curr Drug Targets 2020; 20:501-521. [PMID: 30360733 DOI: 10.2174/1389450119666181022153016] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 06/08/2018] [Accepted: 08/28/2018] [Indexed: 01/21/2023]
Abstract
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
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Affiliation(s)
- Surovi Saikia
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
| | - Manobjyoti Bordoloi
- Natural Products Chemistry Group, CSIR North East Institute of Science & Technology, Jorhat-785006, Assam, India
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9
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Nnadi CI, Jenkins ML, Gentile DR, Bateman LA, Zaidman D, Balius TE, Nomura DK, Burke JE, Shokat KM, London N. Novel K-Ras G12C Switch-II Covalent Binders Destabilize Ras and Accelerate Nucleotide Exchange. J Chem Inf Model 2018; 58:464-471. [PMID: 29320178 DOI: 10.1021/acs.jcim.7b00399] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The success of targeted covalent inhibitors in the global pharmaceutical industry has led to a resurgence of covalent drug discovery. However, covalent inhibitor design for flexible binding sites remains a difficult task due to a lack of methodological development. Here, we compared covalent docking to empirical electrophile screening against the highly dynamic target K-RasG12C. While the overall hit rate of both methods was comparable, we were able to rapidly progress a docking hit to a potent irreversible covalent binder that modifies the inactive, GDP-bound state of K-RasG12C. Hydrogen-deuterium exchange mass spectrometry was used to probe the protein dynamics of compound binding to the switch-II pocket and subsequent destabilization of the nucleotide-binding region. SOS-mediated nucleotide exchange assays showed that, contrary to prior switch-II pocket inhibitors, these new compounds appear to accelerate nucleotide exchange. This study highlights the efficiency of covalent docking as a tool for the discovery of chemically novel hits against challenging targets.
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Affiliation(s)
- Chimno I Nnadi
- Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco , San Francisco, California 94158, United States
| | - Meredith L Jenkins
- Department of Biochemistry and Microbiology. University of Victoria , Victoria, BC V8W 2Y2, Canada
| | - Daniel R Gentile
- Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco , San Francisco, California 94158, United States
| | - Leslie A Bateman
- Departments of Chemistry, Molecular and Cell Biology, and Nutritional Sciences and Toxicology, University of California, Berkeley , Berkeley, California 94720, United States
| | - Daniel Zaidman
- Department of Organic Chemistry, The Weizmann Institute of Science , Rehovot, 7610001, Israel
| | - Trent E Balius
- Department of Pharmaceutical Chemistry, University of California, San Francisco , San Francisco, California 94158, United States
| | - Daniel K Nomura
- Departments of Chemistry, Molecular and Cell Biology, and Nutritional Sciences and Toxicology, University of California, Berkeley , Berkeley, California 94720, United States
| | - John E Burke
- Department of Biochemistry and Microbiology. University of Victoria , Victoria, BC V8W 2Y2, Canada
| | - Kevan M Shokat
- Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco , San Francisco, California 94158, United States
| | - Nir London
- Department of Organic Chemistry, The Weizmann Institute of Science , Rehovot, 7610001, Israel
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10
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Gianti E, Carnevale V. Computational Approaches to Studying Voltage-Gated Ion Channel Modulation by General Anesthetics. Methods Enzymol 2018; 602:25-59. [DOI: 10.1016/bs.mie.2018.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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11
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Sakkal LA, Rajkowski KZ, Armen RS. Prediction of consensus binding mode geometries for related chemical series of positive allosteric modulators of adenosine and muscarinic acetylcholine receptors. J Comput Chem 2017; 38:1209-1228. [PMID: 28130813 PMCID: PMC5403616 DOI: 10.1002/jcc.24728] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2016] [Revised: 11/29/2016] [Accepted: 12/20/2016] [Indexed: 12/13/2022]
Abstract
Following insights from recent crystal structures of the muscarinic acetylcholine receptor, binding modes of Positive Allosteric Modulators (PAMs) were predicted under the assumption that PAMs should bind to the extracellular surface of the active state. A series of well-characterized PAMs for adenosine (A1 R, A2A R, A3 R) and muscarinic acetylcholine (M1 R, M5 R) receptors were modeled using both rigid and flexible receptor CHARMM-based molecular docking. Studies of adenosine receptors investigated the molecular basis of the probe-dependence of PAM activity by modeling in complex with specific agonist radioligands. Consensus binding modes map common pharmacophore features of several chemical series to specific binding interactions. These models provide a rationalization of how PAM binding slows agonist radioligand dissociation kinetics. M1 R PAMs were predicted to bind in the analogous M2 R PAM LY2119620 binding site. The M5 R NAM (ML-375) was predicted to bind in the PAM (ML-380) binding site with a unique induced-fit receptor conformation. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Leon A. Sakkal
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, 901 Walnut St. Suite 918. Philadelphia, PA 19170
| | - Kyle Z. Rajkowski
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, 901 Walnut St. Suite 918. Philadelphia, PA 19170
| | - Roger S. Armen
- Department of Pharmaceutical Sciences, College of Pharmacy, Thomas Jefferson University, 901 Walnut St. Suite 918. Philadelphia, PA 19170
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12
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Incorporation of side chain flexibility into protein binding pockets using MTflex. Bioorg Med Chem 2016; 24:4978-4987. [DOI: 10.1016/j.bmc.2016.08.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 08/16/2016] [Accepted: 08/18/2016] [Indexed: 01/15/2023]
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13
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Abstract
Interest in the application of molecular dynamics (MD) simulations has increased in the field of protein kinase (PK) drug discovery. PKs belong to an important drug target class because they are directly involved in a number of diseases, including cancer. MD methods simulate dynamic biological and chemical events at an atomic level. This information can be combined with other in silico and experimental methods to efficiently target selected receptors. In this review, we present common and advanced methods of MD simulations and we focus on the recent applications of MD-based methodologies that provided significant insights into the elucidation of biological mechanisms involving PKs and into the discovery of novel kinase inhibitors.
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14
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Gagnon JK, Law SM, Brooks CL. Flexible CDOCKER: Development and application of a pseudo-explicit structure-based docking method within CHARMM. J Comput Chem 2016; 37:753-62. [PMID: 26691274 PMCID: PMC4776757 DOI: 10.1002/jcc.24259] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 10/21/2015] [Accepted: 10/23/2015] [Indexed: 01/14/2023]
Abstract
Protein-ligand docking is a commonly used method for lead identification and refinement. While traditional structure-based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an interconverting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well-appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid-based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced-fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re-docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs.
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Affiliation(s)
- Jessica K. Gagnon
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan
| | - Sean M. Law
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan
| | - Charles L. Brooks
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan, Fax: 734-647-1604
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15
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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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16
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Nedumpully-Govindan P, Jemec DB, Ding F. CSAR Benchmark of Flexible MedusaDock in Affinity Prediction and Nativelike Binding Pose Selection. J Chem Inf Model 2015; 56:1042-52. [PMID: 26252196 DOI: 10.1021/acs.jcim.5b00303] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
While molecular docking with both ligand and receptor flexibilities can help capture conformational changes upon binding, correct ranking of nativelike binding poses and accurate estimation of binding affinities remains a major challenge. In addition to the commonly used scoring approach with intermolecular interaction energies, we included the contribution of intramolecular energies changes upon binding in our flexible docking method, MedusaDock. In CSAR 2013-2014 binding prediction benchmark exercises, the new scoring function MScomplex was found to better recapitulate experimental binding affinities and correctly identify ligand-binding sequences from decoy receptors. Our further analysis with the DUD data sets indicates significant improvement of virtual screening enrichment using the new scoring function when compared to the previous intermolecular energy based scoring method. Our postanalysis also suggests a new approach to select nativelike poses in the clustering-based pose ranking approach by MedusaDock. Since the calculation of intramolecular energy changes and clustering-based pose ranking and selection are not MedusaDock specific, we expect a broad application in force-field based estimation of binding affinities and pose ranking using flexible ligand-receptor docking.
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Affiliation(s)
- Praveen Nedumpully-Govindan
- Department of Physics and Astronomy and ‡Department of Genetics and Biochemistry, Clemson University , Clemson, South Carolina 29634, United States
| | - Domen B Jemec
- Department of Physics and Astronomy and ‡Department of Genetics and Biochemistry, Clemson University , Clemson, South Carolina 29634, United States
| | - Feng Ding
- Department of Physics and Astronomy and ‡Department of Genetics and Biochemistry, Clemson University , Clemson, South Carolina 29634, United States
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17
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Abstract
It is widely accepted that protein receptors exist as an ensemble of conformations in solution. How best to incorporate receptor flexibility into virtual screening protocols used for drug discovery remains a significant challenge. Here, stepwise methodologies are described to generate and select relevant protein conformations for virtual screening in the context of the relaxed complex scheme (RCS), to design small molecule libraries for docking, and to perform statistical analyses on the virtual screening results. Methods include equidistant spacing, RMSD-based clustering, and QR factorization protocols for ensemble generation and ROC analysis for ensemble selection.
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18
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Buonfiglio R, Recanatini M, Masetti M. Protein Flexibility in Drug Discovery: From Theory to Computation. ChemMedChem 2015; 10:1141-8. [PMID: 25891095 DOI: 10.1002/cmdc.201500086] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Indexed: 01/01/2023]
Abstract
Nowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery.
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Affiliation(s)
- Rosa Buonfiglio
- Computational Chemistry, Chemistry Innovation Centre, Discovery Sciences, AstraZeneca R&D Mölndal, 43183 Mölndal (Sweden)
| | - Maurizio Recanatini
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, 40126 Bologna (Italy)
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum-University of Bologna, Via Belmeloro 6, 40126 Bologna (Italy).
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19
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Karaman B, Sippl W. Docking and binding free energy calculations of sirtuin inhibitors. Eur J Med Chem 2015; 93:584-98. [PMID: 25748123 DOI: 10.1016/j.ejmech.2015.02.045] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2014] [Revised: 01/25/2015] [Accepted: 02/22/2015] [Indexed: 01/24/2023]
Abstract
Sirtuins form a unique and highly conserved class of NAD(+)-dependent lysine deacylases. Among these the human subtypes Sirt1-3 has been implicated in the pathogenesis of numerous diseases such as cancer, metabolic syndromes, viral diseases and neurological disorders. Most of the sirtuin inhibitors that have been identified so far show limited potency and/or isoform selectivity. Here, we introduce a promising method to generate protein-inhibitor complexes of human Sirt1, Sirt2 and Sirt3 by means of ligand docking and molecular dynamics simulations. This method highly reduces the complexity of such applications and can be applied to other protein targets beside sirtuins. To the best of our knowledge, we present the first binding free energy method developed by using a validated data set of sirtuin inhibitors, where both a fair number of compounds (33 thieno[3,2-d]pyrimidine-6-carboxamide derivatives) was developed and tested in the same laboratory and also crystal structures in complex with the enzyme have been reported. A significant correlation between binding free energies derived from MM-GBSA calculations and in vitro data was found for all three sirtuin subtypes. The developed MM-GBSA protocol is computationally inexpensive and can be applied as a post-docking filter in virtual screening to find novel Sirt1-3 inhibitors as well as to prioritize compounds with similar chemical structures for further biological characterization.
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Affiliation(s)
- Berin Karaman
- Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle, Saale, Germany
| | - Wolfgang Sippl
- Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, 06120 Halle, Saale, Germany.
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20
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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: 103] [Impact Index Per Article: 10.3] [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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21
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Masini T, Pilger J, Kroezen BS, Illarionov B, Lottmann P, Fischer M, Griesinger C, Hirsch AKH. De novo fragment-based design of inhibitors of DXS guided by spin-diffusion-based NMR spectroscopy. Chem Sci 2014. [DOI: 10.1039/c4sc00588k] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
A ligand-based NMR methodology (STI) enabled de novo fragment-based design of inhibitors of the enzyme DXS present in the non-mevalonate pathway in the absence of X-ray co-crystal structures.
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Affiliation(s)
- T. Masini
- Stratingh Institute for Chemistry
- University of Groningen
- NL-9747 AG Groningen, The Netherlands
| | - J. Pilger
- Max-Planck-Institute for Biophysical Chemisty
- 37077 Göttingen, Germany
| | - B. S. Kroezen
- Stratingh Institute for Chemistry
- University of Groningen
- NL-9747 AG Groningen, The Netherlands
| | - B. Illarionov
- Hamburg School of Food Science
- Institute of Food Chemistry
- Hamburg, Germany
| | - P. Lottmann
- Max-Planck-Institute for Biophysical Chemisty
- 37077 Göttingen, Germany
| | - M. Fischer
- Hamburg School of Food Science
- Institute of Food Chemistry
- Hamburg, Germany
| | - C. Griesinger
- Max-Planck-Institute for Biophysical Chemisty
- 37077 Göttingen, Germany
| | - A. K. H. Hirsch
- Stratingh Institute for Chemistry
- University of Groningen
- NL-9747 AG Groningen, The Netherlands
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22
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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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23
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Zhang J, Wang S, Li Y, Xu P, Chen F, Tan Y, Duan J. Anti-diarrheal constituents of Alpinia oxyphylla. Fitoterapia 2013; 89:149-56. [DOI: 10.1016/j.fitote.2013.04.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Revised: 04/03/2013] [Accepted: 04/05/2013] [Indexed: 12/24/2022]
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24
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Zhu S, Travis SM, Elcock AH. Accurate calculation of mutational effects on the thermodynamics of inhibitor binding to p38α MAP kinase: a combined computational and experimental study. J Chem Theory Comput 2013; 9:3151-3164. [PMID: 23914145 PMCID: PMC3731164 DOI: 10.1021/ct400104x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A major current challenge for drug design efforts focused on protein kinases is the development of drug resistance caused by spontaneous mutations in the kinase catalytic domain. The ubiquity of this problem means that it would be advantageous to develop fast, effective computational methods that could be used to determine the effects of potential resistance-causing mutations before they arise in a clinical setting. With this long-term goal in mind, we have conducted a combined experimental and computational study of the thermodynamic effects of active-site mutations on a well-characterized and high-affinity interaction between a protein kinase and a small-molecule inhibitor. Specifically, we developed a fluorescence-based assay to measure the binding free energy of the small-molecule inhibitor, SB203580, to the p38α MAP kinase and used it measure the inhibitor's affinity for five different kinase mutants involving two residues (Val38 and Ala51) that contact the inhibitor in the crystal structure of the inhibitor-kinase complex. We then conducted long, explicit-solvent thermodynamic integration (TI) simulations in an attempt to reproduce the experimental relative binding affinities of the inhibitor for the five mutants; in total, a combined simulation time of 18.5 μs was obtained. Two widely used force fields - OPLS-AA/L and Amber ff99SB-ILDN - were tested in the TI simulations. Both force fields produced excellent agreement with experiment for three of the five mutants; simulations performed with the OPLS-AA/L force field, however, produced qualitatively incorrect results for the constructs that contained an A51V mutation. Interestingly, the discrepancies with the OPLS-AA/L force field could be rectified by the imposition of position restraints on the atoms of the protein backbone and the inhibitor without destroying the agreement for other mutations; the ability to reproduce experiment depended, however, upon the strength of the restraints' force constant. Imposition of position restraints in corresponding simulations that used the Amber ff99SB-ILDN force field had little effect on their ability to match experiment. Overall, the study shows that both force fields can work well for predicting the effects of active-site mutations on small molecule binding affinities and demonstrates how a direct combination of experiment and computation can be a powerful strategy for developing an understanding of protein-inhibitor interactions.
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Affiliation(s)
- Shun Zhu
- Department of Biochemistry, University of Iowa, Iowa City, IA 52242
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25
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Zhang M, Pascal JM, Schumann M, Armen RS, Zhang JF. Identification of the functional binding pocket for compounds targeting small-conductance Ca²⁺-activated potassium channels. Nat Commun 2013; 3:1021. [PMID: 22929778 PMCID: PMC3563359 DOI: 10.1038/ncomms2017] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Accepted: 07/20/2012] [Indexed: 12/14/2022] Open
Abstract
Small- and intermediate-conductance Ca2+-activated potassium channels, activated by Ca2+-bound calmodulin, play an important role in regulating membrane excitability. These channels are also linked to clinical abnormalities. A tremendous amount of effort has been devoted to developing small molecule compounds targeting these channels. However, these compounds often suffer from low potency and lack of selectivity, hindering their potentials for clinical use. A key contributing factor is the lack of knowledge of the binding site(s) for these compounds. Here we demonstrate by X-ray crystallography that the binding pocket for the compounds of the 1-EBIO class is located at the calmodulin-channel interface. We show that, based on structure data and molecular docking, mutations of the channel can effectively change the potency of these compounds. Our results provide insight into the molecular nature of the binding pocket and its contribution to the potency and selectivity of the compounds of the 1-EBIO class.
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Affiliation(s)
- Miao Zhang
- Department of Molecular Physiology and Biophysics, Thomas Jefferson University, 1020 Locust Street, Philadelphia, Pennsylvania 19107, USA
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26
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Ben Nasr N, Guillemain H, Lagarde N, Zagury JF, Montes M. Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query. J Chem Inf Model 2013; 53:293-311. [PMID: 23312043 DOI: 10.1021/ci3004557] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A(3), the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These "binding site properties-based" guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available.
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Affiliation(s)
- Nesrine Ben Nasr
- Laboratoire Génomique Bioinformatique et Applications, Équipe d'accueil EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003 Paris, France
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27
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Abstract
Virtual screening has become a standard tool in drug discovery to identify novel lead compounds that target a biomolecule of interest. I present several concepts in ligand-based and structure-based virtual screening and discuss some of the current shortcomings and new developments. I also highlight approaches that combine concepts from structure- and ligand-based design.
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Affiliation(s)
- Markus Lill
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN, USA
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28
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Saranya N, Jeyakanthan J, Selvaraj S. Impact of protein binding cavity volume (PCV) and ligand volume (LV) in rigid and flexible docking of protein–ligand complexes. Bioorg Med Chem Lett 2012; 22:7593-7. [DOI: 10.1016/j.bmcl.2012.10.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2012] [Revised: 09/26/2012] [Accepted: 10/02/2012] [Indexed: 11/28/2022]
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29
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Flick J, Tristram F, Wenzel W. Modeling loop backbone flexibility in receptor-ligand docking simulations. J Comput Chem 2012; 33:2504-15. [DOI: 10.1002/jcc.23087] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2012] [Revised: 06/15/2012] [Accepted: 07/09/2012] [Indexed: 12/20/2022]
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30
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Osguthorpe DJ, Sherman W, Hagler AT. Generation of Receptor Structural Ensembles for Virtual Screening Using Binding Site Shape Analysis and Clustering. Chem Biol Drug Des 2012; 80:182-93. [DOI: 10.1111/j.1747-0285.2012.01396.x] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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31
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Vinh NB, Simpson JS, Scammells PJ, Chalmers DK. Virtual screening using a conformationally flexible target protein: models for ligand binding to p38α MAPK. J Comput Aided Mol Des 2012; 26:409-23. [PMID: 22527960 DOI: 10.1007/s10822-012-9569-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Accepted: 03/26/2012] [Indexed: 12/25/2022]
Abstract
We have used virtual screening to develop models for the binding of aryl substituted heterocycles to p38α MAPK. Virtual screening was conducted on a number of p38α MAPK crystal structures using a library of 46 known p38α MAPK inhibitors containing a heterocyclic core substituted by pyridine and fluorophenyl rings (structurally related to SB203580) and a set of decoy compounds. Multiple protonation states and tautomers of active and decoy compounds were considered. Each docking model was evaluated using receiver operating characteristic (ROC) curves and enrichment factors. The two best performing single crystal structures were found to be 1BL7 and 2EWA, with enrichment factors of 14.1 and 13.0 at 2% of the virtual screen respectively. Ensembles of up to four receptors of similar conformations were generated, generally giving good or very good performances with high ROC AUCs and good enrichment. The 1BL7-2EWA ensemble was able to outperform each of its constituent receptors and gave high enrichment factors of 17.3, 12.0, 8.0 at 2, 5 and 10% respectively, of the virtual screen. A ROC AUC of 0.94 was obtained for this ensemble. This method may be applied to other proteins where there are a large number of inhibitor classes with different binding site conformations.
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Affiliation(s)
- Natalie B Vinh
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University (Parkville Campus), 381 Royal Parade, Parkville, VIC, 3052, Australia
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32
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Asses Y, Venkatraman V, Leroux V, Ritchie DW, Maigret B. Exploring c-Met kinase flexibility by sampling and clustering its conformational space. Proteins 2012; 80:1227-38. [PMID: 22275094 DOI: 10.1002/prot.24021] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2011] [Revised: 11/14/2011] [Accepted: 12/13/2011] [Indexed: 11/09/2022]
Abstract
It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met.
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Affiliation(s)
- Yasmine Asses
- Nancy Université, LORIA/UMR 7503, Équipe-projet Orpailleur, Campus Scientifique, Vandœuvre-lès-Nancy Cedex, France
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33
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Xu M, Lill MA. Utilizing experimental data for reducing ensemble size in flexible-protein docking. J Chem Inf Model 2011; 52:187-98. [PMID: 22146074 DOI: 10.1021/ci200428t] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Efficient and sufficient incorporation of protein flexibility into docking is still a challenging task. Docking to an ensemble of protein structures has proven its utility for docking, but using a large ensemble of structures can reduce the efficiency of docking and can increase the number of false positives in virtual screening. In this paper, we describe the application of our new methodology, Limoc, to generate an ensemble of holo-like protein structures in combination with the relaxed complex scheme (RCS), to virtual screening. We describe different schemes to reduce the ensemble of protein structures to increase efficiency and enrichment quality. Utilizing experimental knowledge about actives for a target protein allows the reduction of ensemble members to a minimum of three protein structures, increasing enrichment quality and efficiency simultaneously.
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Affiliation(s)
- Mengang Xu
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47907, USA
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34
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O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011; 3:33. [PMID: 21982300 PMCID: PMC3198950 DOI: 10.1186/1758-2946-3-33] [Citation(s) in RCA: 5105] [Impact Index Per Article: 392.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2011] [Accepted: 10/07/2011] [Indexed: 02/08/2023] Open
Abstract
Background A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. Results We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. Conclusions Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
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Affiliation(s)
- Noel M O'Boyle
- University of Pittsburgh, Department of Chemistry, 219 Parkman Avenue, Pittsburgh, PA 15217, USA.
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35
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O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011. [PMID: 21982300 DOI: 10.1186/1758-2946-3-33.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. RESULTS We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. CONCLUSIONS Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
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Affiliation(s)
- Noel M O'Boyle
- University of Pittsburgh, Department of Chemistry, 219 Parkman Avenue, Pittsburgh, PA 15217, USA.
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36
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Lill MA. Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry 2011; 50:6157-69. [PMID: 21678954 DOI: 10.1021/bi2004558] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Flexibility and dynamics are protein characteristics that are essential for the process of molecular recognition. Conformational changes in the protein that are coupled to ligand binding are described by the biophysical models of induced fit and conformational selection. Different concepts that incorporate protein flexibility into protein-ligand docking within the context of these two models are reviewed. Several computational studies that discuss the validity and possible limitations of such approaches will be presented. Finally, different approaches that incorporate protein dynamics, e.g., configurational entropy, and solvation effects into docking will be highlighted.
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Affiliation(s)
- Markus A Lill
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47907, United States.
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37
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Rahaman O, Estrada TP, Doren DJ, Taufer M, Brooks CL, Armen RS. Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy. J Chem Inf Model 2011; 51:2047-65. [PMID: 21644546 DOI: 10.1021/ci1003009] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.
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Affiliation(s)
- Obaidur Rahaman
- Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716, United States
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38
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Nichols SE, Baron R, Ivetac A, McCammon JA. Predictive power of molecular dynamics receptor structures in virtual screening. J Chem Inf Model 2011; 51:1439-46. [PMID: 21534609 PMCID: PMC3124922 DOI: 10.1021/ci200117n] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard, evaluating two well-characterized systems of varying flexibility in ligand-bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases, MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range.
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Affiliation(s)
- Sara E Nichols
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093-0365, United States.
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39
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Tuccinardi T, Botta M, Giordano A, Martinelli A. Protein kinases: docking and homology modeling reliability. J Chem Inf Model 2010; 50:1432-41. [PMID: 20726600 DOI: 10.1021/ci100161z] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A database of about 700 high-resolution kinase structures was used to test the reliability of 17 docking procedures (using six docking software packages) by means of self- and cross-docking studies. The analysis of about 80 000 docking calculations suggests that the docking of an unknown ligand into a kinase has a probability of only 30-37% to be a correct ligand pose. However, based on the hypothesis that docking calculations are more reliable if the ligand to be docked is similar to the ligand present in the complex from which the target docking protein has been extracted, we propose an automated procedure that is able to improve the docking accuracy, suggest the best protein for docking studies, and assess the statistical reliability of docking calculations. The results were also transferred to the homology modeling field and led us to propose an alternative strategy based on ligand similarity for the development of kinase models whose experimental structure was not known. Our results suggest that in many cases this approach can give better results than the classical homology modeling procedure based exclusively on the sequence homology.
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Affiliation(s)
- Tiziano Tuccinardi
- Dipartimento di Scienze Farmaceutiche, Universita di Pisa, via Bonanno 6, 56126 Pisa, Italy
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40
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Yuriev E, Agostino M, Ramsland PA. Challenges and advances in computational docking: 2009 in review. J Mol Recognit 2010; 24:149-64. [DOI: 10.1002/jmr.1077] [Citation(s) in RCA: 223] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2010] [Revised: 07/20/2010] [Accepted: 07/21/2010] [Indexed: 12/12/2022]
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41
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Novoa EM, Pouplana LRD, Barril X, Orozco M. Ensemble Docking from Homology Models. J Chem Theory Comput 2010; 6:2547-57. [DOI: 10.1021/ct100246y] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Eva Maria Novoa
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
| | - Lluis Ribas de Pouplana
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
| | - Xavier Barril
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
| | - Modesto Orozco
- Joint IRB-BSC Research Program in Computational Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Spain, Cell and Developmental Biology, Institute for Research in Biomedicine, Josep Samitier 1−5, Barcelona 08028, Institució Catalana per la Recerca i Estudis Avançats, Passeig Lluis Companys 23, Barcelona 08010, Spain, Departament de Fisicoquímica, Facultat de Farmàcia, Avgda Diagonal sn, Barcelona 08028, Spain, and Structural Bioinformatics Node Instituto Nacional de
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