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Modeling receptor flexibility in the structure-based design of KRAS G12C inhibitors. J Comput Aided Mol Des 2022; 36:591-604. [PMID: 35930206 PMCID: PMC9512760 DOI: 10.1007/s10822-022-00467-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/15/2022] [Indexed: 11/15/2022]
Abstract
KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRASG12C has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRASG12C—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRASG12C. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRASG12C, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRASG12C inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.
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2
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Gao M, Moumbock AFA, Qaseem A, Xu Q, Günther S. CovPDB: a high-resolution coverage of the covalent protein-ligand interactome. Nucleic Acids Res 2021; 50:D445-D450. [PMID: 34581813 PMCID: PMC8728183 DOI: 10.1093/nar/gkab868] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/07/2021] [Accepted: 09/15/2021] [Indexed: 01/14/2023] Open
Abstract
In recent years, the drug discovery paradigm has shifted toward compounds that covalently modify disease-associated target proteins, because they tend to possess high potency, selectivity, and duration of action. The rational design of novel targeted covalent inhibitors (TCIs) typically starts from resolved macromolecular structures of target proteins in their apo or holo forms. However, the existing TCI databases contain only a paucity of covalent protein–ligand (cP–L) complexes. Herein, we report CovPDB, the first database solely dedicated to high-resolution cocrystal structures of biologically relevant cP–L complexes, curated from the Protein Data Bank. For these curated complexes, the chemical structures and warheads of pre-reactive electrophilic ligands as well as the covalent bonding mechanisms to their target proteins were expertly manually annotated. Totally, CovPDB contains 733 proteins and 1,501 ligands, relating to 2,294 cP–L complexes, 93 reactive warheads, 14 targetable residues, and 21 covalent mechanisms. Users are provided with an intuitive and interactive web interface that allows multiple search and browsing options to explore the covalent interactome at a molecular level in order to develop novel TCIs. CovPDB is freely accessible at http://www.pharmbioinf.uni-freiburg.de/covpdb/ and its contents are available for download as flat files of various formats.
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Affiliation(s)
- Mingjie Gao
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Aurélien F A Moumbock
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Ammar Qaseem
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Qianqing Xu
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Stefan Günther
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
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3
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Abstract
As a member of small GTPase family, KRAS protein is a key physiological modulator of various cellular activities including proliferation. However, mutations of KRAS present in numerous cancer types, most frequently in pancreatic (> 60%), colorectal (> 40%), and lung cancers, drive oncogenic processes through overactivation of proliferation. The G12C mutation of KRAS protein is especially abundant in the case of these types of malignancies. Despite its key importance in human disease, KRAS was assumed to be non-druggable for a long time since the protein seemingly lacks potential drug-binding pockets except the nucleotide-binding site, which is difficult to be targeted due to the high affinity of KRAS for both GDP and GTP. Recently, a new approach broke the ice and provided evidence that upon covalent targeting of the G12C mutant KRAS, a highly dynamic pocket was revealed. This novel targeting is especially important since it serves with an inherent solution for drug selectivity. Based on these results, various structure-based drug design projects have been launched to develop selective KRAS mutant inhibitors. In addition to the covalent modification strategy mostly applicable for G12C mutation, different innovative solutions have been suggested for the other frequently occurring oncogenic G12 mutants. Here we summarize the latest advances of this field, provide perspectives for novel approaches, and highlight the special properties of KRAS, which might issue some new challenges.
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Affiliation(s)
- Kinga Nyíri
- Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, 1111, Hungary.
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, 1117, Hungary.
| | - Gergely Koppány
- Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, 1111, Hungary
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, 1117, Hungary
| | - Beáta G Vértessy
- Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, 1111, Hungary.
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, 1117, Hungary.
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4
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Zaidman D, Gehrtz P, Filep M, Fearon D, Gabizon R, Douangamath A, Prilusky J, Duberstein S, Cohen G, Owen CD, Resnick E, Strain-Damerell C, Lukacik P, Barr H, Walsh MA, von Delft F, London N. An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 M pro inhibitor. Cell Chem Biol 2021; 28:1795-1806.e5. [PMID: 34174194 PMCID: PMC8228784 DOI: 10.1016/j.chembiol.2021.05.018] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/24/2021] [Accepted: 05/27/2021] [Indexed: 01/20/2023]
Abstract
Designing covalent inhibitors is increasingly important, although it remains challenging. Here, we present covalentizer, a computational pipeline for identifying irreversible inhibitors based on structures of targets with non-covalent binders. Through covalent docking of tailored focused libraries, we identify candidates that can bind covalently to a nearby cysteine while preserving the interactions of the original molecule. We found ∼11,000 cysteines proximal to a ligand across 8,386 complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In a prospective evaluation, five out of nine predicted covalent kinase inhibitors showed half-maximal inhibitory concentration (IC50) values between 155 nM and 4.5 μM. Application against an existing SARS-CoV Mpro reversible inhibitor led to an acrylamide inhibitor series with low micromolar IC50 values against SARS-CoV-2 Mpro. The docking was validated by 12 co-crystal structures. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.
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Affiliation(s)
- Daniel Zaidman
- Department of Chemical and Structural Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Paul Gehrtz
- Department of Chemical and Structural Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Mihajlo Filep
- Department of Chemical and Structural Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Daren Fearon
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
| | - Ronen Gabizon
- Department of Chemical and Structural Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Alice Douangamath
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK
| | - Jaime Prilusky
- Life Sciences Core Facilities, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Shirly Duberstein
- Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, The Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Galit Cohen
- Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, The Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - C David Owen
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK; Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Efrat Resnick
- Department of Chemical and Structural Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Claire Strain-Damerell
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK; Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Petra Lukacik
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK; Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | | | - Haim Barr
- Wohl Institute for Drug Discovery of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, The Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Martin A Walsh
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK; Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK
| | - Frank von Delft
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot OX11 0QX, UK; Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot OX11 0FA, UK; Structural Genomics Consortium, University of Oxford, Old Road Campus, Roosevelt Drive, Headington OX3 7DQ, UK; Department of Biochemistry, University of Johannesburg, Auckland Park 2006, South Africa
| | - Nir London
- Department of Chemical and Structural Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel.
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5
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FitzGerald EA, Butko MT, Boronat P, Cederfelt D, Abramsson M, Ludviksdottir H, van Muijlwijk-Koezen JE, de Esch IJP, Dobritzsch D, Young T, Danielson UH. Discovery of fragments inducing conformational effects in dynamic proteins using a second-harmonic generation biosensor. RSC Adv 2021; 11:7527-7537. [PMID: 35423271 PMCID: PMC8694943 DOI: 10.1039/d0ra09844b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 01/28/2021] [Indexed: 01/13/2023] Open
Abstract
Biophysical screening of compound libraries for the identification of ligands that interact with a protein is efficient, but does typically not reveal if (or how) ligands may interfere with its functional properties. For this a biochemical/functional assay is required. But for proteins whose function is dependent on a conformational change, such assays are typically complex or have low throughput. Here we have explored a high-throughput second-harmonic generation (SHG) biosensor to detect fragments that induce conformational changes upon binding to a protein in real time and identify dynamic regions. Multiwell plate format SHG assays were developed for wild-type and six engineered single-cysteine mutants of acetyl choline binding protein (AChBP), a homologue to ligand gated ion channels (LGICs). They were conjugated with second harmonic-active labels via amine or maleimide coupling. To validate the assay, it was confirmed that the conformational changes induced in AChBP by nicotinic acetyl choline receptor (nAChR) agonists and antagonists were qualitatively different. A 1056 fragment library was subsequently screened against all variants and conformational modulators of AChBP were successfully identified, with hit rates from 9-22%, depending on the AChBP variant. A subset of four hits was selected for orthogonal validation and structural analysis. A time-resolved grating-coupled interferometry-based biosensor assay confirmed the interaction to be a reversible 1-step 1 : 1 interaction, and provided estimates of affinities and interaction kinetic rate constants (K D = 0.28-63 μM, k a = 0.1-6 μM-1 s-1, k d = 1 s-1). X-ray crystallography of two of the fragments confirmed their binding at a previously described conformationally dynamic site, corresponding to the regulatory site of LGICs. These results reveal that SHG has the sensitivity to identify fragments that induce conformational changes in a protein. A selection of fragment hits with a response profile different to known LGIC regulators was characterized and confirmed to bind to dynamic regions of the protein.
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Affiliation(s)
- Edward A FitzGerald
- Department of Chemistry - BMC, Uppsala University Uppsala 751 23 Sweden
- Beactica Therapeutics Virdings allé 2 Uppsala 754 40 Sweden
| | | | - Pierre Boronat
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam De Boelelaan 1108 1081 HZ Amsterdam The Netherlands
| | - Daniela Cederfelt
- Department of Chemistry - BMC, Uppsala University Uppsala 751 23 Sweden
| | - Mia Abramsson
- Department of Chemistry - BMC, Uppsala University Uppsala 751 23 Sweden
| | | | - Jacqueline E van Muijlwijk-Koezen
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam De Boelelaan 1108 1081 HZ Amsterdam The Netherlands
| | - Iwan J P de Esch
- Amsterdam Institute of Molecular and Life Sciences (AIMMS), Division of Medicinal Chemistry, Faculty of Science, Vrije Universiteit Amsterdam De Boelelaan 1108 1081 HZ Amsterdam The Netherlands
| | - Doreen Dobritzsch
- Department of Chemistry - BMC, Uppsala University Uppsala 751 23 Sweden
| | - Tracy Young
- Biodesy, Inc. 170 Harbor Way South San Francisco 94080 CA USA
| | - U Helena Danielson
- Department of Chemistry - BMC, Uppsala University Uppsala 751 23 Sweden
- Science for Life Laboratory, Uppsala University Sweden
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6
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Lu W, Kostic M, Zhang T, Che J, Patricelli MP, Jones LH, Chouchani ET, Gray NS. Fragment-based covalent ligand discovery. RSC Chem Biol 2021; 2:354-367. [PMID: 34458789 PMCID: PMC8341086 DOI: 10.1039/d0cb00222d] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/22/2021] [Accepted: 01/20/2021] [Indexed: 12/15/2022] Open
Abstract
Targeted covalent inhibitors have regained widespread attention in drug discovery and have emerged as powerful tools for basic biomedical research. Fueled by considerable improvements in mass spectrometry sensitivity and sample processing, chemoproteomic strategies have revealed thousands of proteins that can be covalently modified by reactive small molecules. Fragment-based drug discovery, which has traditionally been used in a target-centric fashion, is now being deployed on a proteome-wide scale thereby expanding its utility to both the discovery of novel covalent ligands and their cognate protein targets. This powerful approach is allowing ‘high-throughput’ serendipitous discovery of cryptic pockets leading to the identification of pharmacological modulators of proteins previously viewed as “undruggable”. The reactive fragment toolkit has been enabled by recent advances in the development of new chemistries that target residues other than cysteine including lysine and tyrosine. Here, we review the emerging area of covalent fragment-based ligand discovery, which integrates the benefits of covalent targeting and fragment-based medicinal chemistry. We discuss how the two strategies synergize to facilitate the efficient discovery of new pharmacological modulators of established and new therapeutic target proteins. Covalent fragment-based ligand discovery greatly facilitates the discovery of useful fragments for drug discovery and helps unveil chemical-tractable biological targets in native biological systems.![]()
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Affiliation(s)
- Wenchao Lu
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
| | - Milka Kostic
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA
| | - Tinghu Zhang
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
| | - Jianwei Che
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA.,Center for Protein Degradation, Dana-Farber Cancer Institute Boston MA 02215 USA
| | | | - Lyn H Jones
- Center for Protein Degradation, Dana-Farber Cancer Institute Boston MA 02215 USA
| | - Edward T Chouchani
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Cell Biology, Harvard Medical School Boston MA 02215 USA
| | - Nathanael S Gray
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
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7
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Modeling Covalent Protein-Ligand Interactions. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11519-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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8
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Abstract
INTRODUCTION Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence. AREAS COVERED In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking. EXPERT OPINION There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.
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Affiliation(s)
- Julio Caballero
- Departamento De Bioinformática, Centro De Bioinformática, Simulación Y Modelado (CBSM), Facultad De Ingeniería, Universidad De Talca, Talca, Chile
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9
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Bianco G, Goodsell DS, Forli S. Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors. Trends Pharmacol Sci 2020; 41:1038-1049. [PMID: 33153778 PMCID: PMC7669701 DOI: 10.1016/j.tips.2020.10.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/28/2022]
Abstract
Targeted covalent inhibitors are currently showing great promise for systems that are normally difficult to target with small molecule therapies. This renewed interest has spurred the refinement of existing computational methods as well as the designof new ones, expanding the toolbox for discovery and optimization of selectiveand effective covalent inhibitors. Commonly applied approaches are covalentdocking methods that predict the conformation of the covalent complex with known residues. More recently, a new predictive method, reactive docking, was developed, building on the growing corpus of data generated by large proteomics experiments. This method was successfully used in several 'inverse drug discovery' programs that use high-throughput techniques to isolate effective compounds based on screening of entire compound libraries based on desired phenotypes.
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Affiliation(s)
- Giulia Bianco
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA; Research Collaboratory for Structure Bioinformatics Protein Data Bank, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
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Wang Z, Sun H, Shen C, Hu X, Gao J, Li D, Cao D, Hou T. Combined strategies in structure-based virtual screening. Phys Chem Chem Phys 2020; 22:3149-3159. [PMID: 31995074 DOI: 10.1039/c9cp06303j] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.
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Affiliation(s)
- Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Xueping Hu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Junbo Gao
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Dan Li
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004, Hunan, P. R. China.
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
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11
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Deb I, Frank AT. Accelerating Rare Dissociative Processes in Biomolecules Using Selectively Scaled MD Simulations. J Chem Theory Comput 2019; 15:5817-5828. [PMID: 31509413 DOI: 10.1021/acs.jctc.9b00262] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Molecular dynamics (MD) simulations can be a powerful tool for modeling complex dissociative processes such as ligand unbinding. However, many biologically relevant dissociative processes occur on timescales that far exceed the timescales of typical MD simulations. Here, we implement and apply an enhanced sampling method in which specific energy terms in the potential energy function are selectively "scaled" to accelerate dissociative events during simulations. Using ligand unbinding as an example of a complex dissociative process, we selectively scaled up ligand-water interactions in an attempt to increase the rate of ligand unbinding. After applying our selectively scaled MD (ssMD) approach to several cyclin-dependent kinase-inhibitor complexes, we discovered that we could accelerate ligand unbinding, thereby allowing, in some cases, unbinding events to occur within as little as 2 ns. Moreover, we found that we could make realistic estimates of the initial unbinding times (τunbindsim) as well as the accompanying change in free energy (ΔGsim) of the inhibitors from our ssMD simulation data. To accomplish this, we employed a previously described Kramers'-based rate extrapolation method and a newly described free energy extrapolation method. Because our ssMD approach is general, it should find utility as an easy-to-deploy, enhanced sampling method for modeling other dissociative processes.
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12
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Rachman M, Scarpino A, Bajusz D, Pálfy G, Vida I, Perczel A, Barril X, Keserű GM. DUckCov: a Dynamic Undocking-Based Virtual Screening Protocol for Covalent Binders. ChemMedChem 2019; 14:1011-1021. [PMID: 30786178 PMCID: PMC6593427 DOI: 10.1002/cmdc.201900078] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Indexed: 12/25/2022]
Abstract
Thanks to recent guidelines, the design of safe and effective covalent drugs has gained significant interest. Other than targeting non‐conserved nucleophilic residues, optimizing the noncovalent binding framework is important to improve potency and selectivity of covalent binders toward the desired target. Significant efforts have been made in extending the computational toolkits to include a covalent mechanism of protein targeting, like in the development of covalent docking methods for binding mode prediction. To highlight the value of the noncovalent complex in the covalent binding process, here we describe a new protocol using tethered and constrained docking in combination with Dynamic Undocking (DUck) as a tool to privilege strong protein binders for the identification of novel covalent inhibitors. At the end of the protocol, dedicated covalent docking methods were used to rank and select the virtual hits based on the predicted binding mode. By validating the method on JAK3 and KRas, we demonstrate how this fast iterative protocol can be applied to explore a wide chemical space and identify potent targeted covalent inhibitors.
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Affiliation(s)
- Moira Rachman
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028, Barcelona, Spain.,Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Andrea Scarpino
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
| | - Gyula Pálfy
- Laboratory of Structural Chemistry and Biology & MTA-ELTE Protein Modelling Research Group, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary
| | - István Vida
- Laboratory of Structural Chemistry and Biology & MTA-ELTE Protein Modelling Research Group, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary
| | - András Perczel
- Laboratory of Structural Chemistry and Biology & MTA-ELTE Protein Modelling Research Group, Eötvös Loránd University, Pázmány Péter sétány 1/A, 1117, Budapest, Hungary
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028, Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010, Barcelona, Spain
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2, 1117, Budapest, Hungary
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