1
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Yu S, Zhang Y, Yang J, Xu H, Lan S, Zhao B, Luo M, Ma X, Zhang H, Wang S, Shen H, Zhang Y, Xu Y, Li R. Discovery of (R)-4-(8-methoxy-2-methyl-1-(1-phenylethy)-1H-imidazo[4,5-c]quinnolin-7-yl)-3,5-dimethylisoxazole as a potent and selective BET inhibitor for treatment of acute myeloid leukemia (AML) guided by FEP calculation. Eur J Med Chem 2024; 263:115924. [PMID: 37992518 DOI: 10.1016/j.ejmech.2023.115924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/28/2023] [Accepted: 10/29/2023] [Indexed: 11/24/2023]
Abstract
The functions of the bromodomain and extra terminal (BET) family of proteins have been proved to be involved in various diseases, particularly the acute myeloid leukemia (AML). In this work, guided by free energy perturbation (FEP) calculation, a methyl group was selected to be attached to the 1H-imidazo[4,5-c]quinoline skeleton, and a series of congeneric compounds were synthesized. Among them, compound 10 demonstrated outstanding activity against BRD4 BD1 with an IC50 value of 1.9 nM and exhibited remarkable antiproliferative effects against MV4-11 cells. The X-ray cocrystal structure proved that 10 occupied the acetylated lysine (KAc) binding cavity and the WPF shelf of BRD4 BD1. Additionally, 10 displayed high selectivity towards BET family members, effectively inhibiting the growth of AML cells, promoting apoptosis, and arresting the cell cycle at the G0/G1 phase. Further mechanistic studies demonstrated that compound 10 could suppress the expression of c-Myc and CDK6 while enhancing the expression of P21, PARP, and cleaved PARP. Moreover, 10 exhibited remarkable pharmacokinetic properties and significant antitumor efficacy in vivo. Therefore, compound 10 may represent a new, potent and selective BET bromodomain inhibitor for the development of therapeutics to treat AML.
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Affiliation(s)
- Su Yu
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yan Zhang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jie Yang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hongrui Xu
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China
| | - Suke Lan
- College of Chemistry & Environment Protection Engineering, Southwest Minzu University, Chengdu, 610041, China
| | - Binyan Zhao
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Meng Luo
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xinyu Ma
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hongjia Zhang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Shirui Wang
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Hui Shen
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China
| | - Yan Zhang
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China
| | - Yong Xu
- Center for Chemical Biology and Drug Discovery, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190 Kaiyuan Avenue, Guangzhou, 510530, China.
| | - Rui Li
- Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, China.
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2
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Herz AM, Kellici T, Morao I, Michel J. Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities. Methods Mol Biol 2024; 2716:241-264. [PMID: 37702943 DOI: 10.1007/978-1-0716-3449-3_11] [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: 09/14/2023]
Abstract
Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.
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Affiliation(s)
- Anna M Herz
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK
| | - Tahsin Kellici
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
- Merck & Co., Inc., Modelling and Informatics, West Point, PA, USA
| | - Inaki Morao
- Evotec (UK) Ltd., In Silico Research and Development, Abingdon, Oxfordshire, UK
| | - Julien Michel
- EaStChem School of Chemistry, Joseph Black Building, University of Edinburgh, Edinburgh, UK.
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3
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Ross GA, Lu C, Scarabelli G, Albanese SK, Houang E, Abel R, Harder ED, Wang L. The maximal and current accuracy of rigorous protein-ligand binding free energy calculations. Commun Chem 2023; 6:222. [PMID: 37838760 PMCID: PMC10576784 DOI: 10.1038/s42004-023-01019-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 10/02/2023] [Indexed: 10/16/2023] Open
Abstract
Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.
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Affiliation(s)
- Gregory A Ross
- Schrödinger Inc, New York, NY, USA.
- Isomorphic Labs, London, UK.
| | - Chao Lu
- Schrödinger Inc, New York, NY, USA
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4
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Sun S, Fushimi M, Rossetti T, Kaur N, Ferreira J, Miller M, Quast J, van den Heuvel J, Steegborn C, Levin LR, Buck J, Myers RW, Kargman S, Liverton N, Meinke PT, Huggins DJ. Scaffold Hopping and Optimization of Small Molecule Soluble Adenyl Cyclase Inhibitors Led by Free Energy Perturbation. J Chem Inf Model 2023; 63:2828-2841. [PMID: 37060320 DOI: 10.1021/acs.jcim.2c01577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
Free energy perturbation is a computational technique that can be used to predict how small changes to an inhibitor structure will affect the binding free energy to its target. In this paper, we describe the utility of free energy perturbation with FEP+ in the hit-to-lead stage of a drug discovery project targeting soluble adenyl cyclase. The project was structurally enabled by X-ray crystallography throughout. We employed free energy perturbation to first scaffold hop to a preferable chemotype and then optimize the binding affinity to sub-nanomolar levels while retaining druglike properties. The results illustrate that effective use of free energy perturbation can enable a drug discovery campaign to progress rapidly from hit to lead, facilitating proof-of-concept studies that enable target validation.
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Affiliation(s)
- Shan Sun
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
| | - Makoto Fushimi
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
| | - Thomas Rossetti
- Department of Pharmacology, Weill Cornell Medicine, New York City, New York 10056, United States
| | - Navpreet Kaur
- Department of Pharmacology, Weill Cornell Medicine, New York City, New York 10056, United States
| | - Jacob Ferreira
- Department of Pharmacology, Weill Cornell Medicine, New York City, New York 10056, United States
| | - Michael Miller
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
| | - Jonathan Quast
- Department of Biochemistry, University of Bayreuth, Bayreuth 95440, Germany
| | | | - Clemens Steegborn
- Department of Biochemistry, University of Bayreuth, Bayreuth 95440, Germany
| | - Lonny R Levin
- Department of Pharmacology, Weill Cornell Medicine, New York City, New York 10056, United States
| | - Jochen Buck
- Department of Pharmacology, Weill Cornell Medicine, New York City, New York 10056, United States
| | - Robert W Myers
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
| | - Stacia Kargman
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
| | - Nigel Liverton
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
| | - Peter T Meinke
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
- Department of Pharmacology, Weill Cornell Medicine, New York City, New York 10056, United States
| | - David J Huggins
- Tri-Institutional Therapeutics Discovery Institute, New York, New York 10021, United States
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, New York 10065, United States
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5
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:molecules28093906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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6
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Xu H. The slow but steady rise of binding free energy calculations in drug discovery. J Comput Aided Mol Des 2023; 37:67-74. [PMID: 36469232 DOI: 10.1007/s10822-022-00494-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.
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Affiliation(s)
- Huafeng Xu
- Roivant Discovery, 151 West 42nd Street, New York, NY, 10036, USA.
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7
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Cappel D, Mozziconacci JC, Braun T, Steinbrecher T. Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen-Deshalogen Matched Molecular Pairs. J Chem Inf Model 2021; 61:3421-3430. [PMID: 34170707 DOI: 10.1021/acs.jcim.1c00290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of <1 kcal/mol despite the semi-automated calculation scheme. We quantify the accuracy of the optimized potential for liquid simulations (OPLS) force field to predict the effect of halogen addition to compounds, a commonly employed chemical modification in the design of drug-like molecules.
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Affiliation(s)
- Daniel Cappel
- Schrödinger GmbH, Glücksteinallee 25, 68163 Mannheim, Germany
| | | | - Tatjana Braun
- Schrödinger GmbH, Thierschstraße 27, 80538 München, Germany
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8
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Lu C, Wu C, Ghoreishi D, Chen W, Wang L, Damm W, Ross GA, Dahlgren MK, Russell E, Von Bargen CD, Abel R, Friesner RA, Harder ED. OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. J Chem Theory Comput 2021; 17:4291-4300. [PMID: 34096718 DOI: 10.1021/acs.jctc.1c00302] [Citation(s) in RCA: 606] [Impact Index Per Article: 202.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Chao Lu
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Chuanjie Wu
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Delaram Ghoreishi
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Wei Chen
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Lingle Wang
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Wolfgang Damm
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Gregory A. Ross
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Markus K. Dahlgren
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Ellery Russell
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | | | - Robert Abel
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
| | - Richard A. Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
| | - Edward D. Harder
- Schrodinger, Incorporated, 120 West 45th Street, New York, New York 10036, United States
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9
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Albanese SK, Chodera JD, Volkamer A, Keng S, Abel R, Wang L. Is Structure-Based Drug Design Ready for Selectivity Optimization? J Chem Inf Model 2020; 60:6211-6227. [PMID: 33119284 PMCID: PMC8310368 DOI: 10.1021/acs.jcim.0c00815] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.
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Affiliation(s)
- Steven K. Albanese
- Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrea Volkamer
- Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin
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10
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Song LF, Merz KM. Evolution of Alchemical Free Energy Methods in Drug Discovery. J Chem Inf Model 2020; 60:5308-5318. [DOI: 10.1021/acs.jcim.0c00547] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Lin Frank Song
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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11
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Ghanakota P, Bos PH, Konze KD, Staker J, Marques G, Marshall K, Leswing K, Abel R, Bhat S. Combining Cloud-Based Free-Energy Calculations, Synthetically Aware Enumerations, and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization. J Chem Inf Model 2020; 60:4311-4325. [PMID: 32484669 DOI: 10.1021/acs.jcim.0c00120] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Phani Ghanakota
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Pieter H. Bos
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Kyle D. Konze
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Joshua Staker
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Gabriel Marques
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Kyle Marshall
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Karl Leswing
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
| | - Sathesh Bhat
- Schrödinger, Inc., 120 West 45th Street, 17th floor, New York, New York 10036, United States
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12
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Hu X, Maffucci I, Contini A. Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations. Curr Med Chem 2020; 26:7598-7622. [DOI: 10.2174/0929867325666180514110824] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 02/26/2018] [Accepted: 04/18/2018] [Indexed: 12/30/2022]
Abstract
Background:
The inclusion of direct effects mediated by water during the ligandreceptor
recognition is a hot-topic of modern computational chemistry applied to drug discovery
and development. Docking or virtual screening with explicit hydration is still debatable,
despite the successful cases that have been presented in the last years. Indeed, how to select
the water molecules that will be included in the docking process or how the included waters
should be treated remain open questions.
Objective:
In this review, we will discuss some of the most recent methods that can be used in
computational drug discovery and drug development when the effect of a single water, or of a
small network of interacting waters, needs to be explicitly considered.
Results:
Here, we analyse the software to aid the selection, or to predict the position, of water
molecules that are going to be explicitly considered in later docking studies. We also present
software and protocols able to efficiently treat flexible water molecules during docking, including
examples of applications. Finally, we discuss methods based on molecular dynamics
simulations that can be used to integrate docking studies or to reliably and efficiently compute
binding energies of ligands in presence of interfacial or bridging water molecules.
Conclusions:
Software applications aiding the design of new drugs that exploit water molecules,
either as displaceable residues or as bridges to the receptor, are constantly being developed.
Although further validation is needed, workflows that explicitly consider water will
probably become a standard for computational drug discovery soon.
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Affiliation(s)
- Xiao Hu
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
| | - Irene Maffucci
- Pasteur, Département de Chimie, École Normale Supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 75005 Paris, France
| | - Alessandro Contini
- Università degli Studi di Milano, Dipartimento di Scienze Farmaceutiche, Sezione di Chimica Generale e Organica “A. Marchesini”, Via Venezian, 21 20133 Milano, Italy
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13
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Aminpour M, Montemagno C, Tuszynski JA. An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications. Molecules 2019; 24:E1693. [PMID: 31052253 PMCID: PMC6539951 DOI: 10.3390/molecules24091693] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 01/29/2023] Open
Abstract
In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.
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Affiliation(s)
- Maral Aminpour
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada.
- Ingenuity Lab, Edmonton, AB T6G 2R3, Canada.
- Department of Oncology, University of Alberta, Edmonton, AB T6G 1Z2, Canada.
| | - Carlo Montemagno
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada.
- Ingenuity Lab, Edmonton, AB T6G 2R3, Canada.
- Southern Illinois University, Carbondale, IL 62901, USA.
| | - Jack A Tuszynski
- Department of Oncology, University of Alberta, Edmonton, AB T6G 1Z2, Canada.
- Department of Physics, University of Alberta, Edmonton, AB T6G 2E1, Canada.
- Department of Mechanical Engineering and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10129 Turin, Italy.
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14
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Pérez-Benito L, Casajuana-Martin N, Jiménez-Rosés M, van Vlijmen H, Tresadern G. Predicting Activity Cliffs with Free-Energy Perturbation. J Chem Theory Comput 2019; 15:1884-1895. [DOI: 10.1021/acs.jctc.8b01290] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Laura Pérez-Benito
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Nil Casajuana-Martin
- Laboratori de Medicina Computacional, Unitat de Bioestadistica, Facultat de Medicina, Universitat Autonoma de Barcelona, Bellaterra 08193, Spain
| | - Mireia Jiménez-Rosés
- Laboratori de Medicina Computacional, Unitat de Bioestadistica, Facultat de Medicina, Universitat Autonoma de Barcelona, Bellaterra 08193, Spain
| | - Herman van Vlijmen
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, Beerse B-2340, Belgium
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15
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Roos K, Wu C, Damm W, Reboul M, Stevenson JM, Lu C, Dahlgren MK, Mondal S, Chen W, Wang L, Abel R, Friesner RA, Harder ED. OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J Chem Theory Comput 2019; 15:1863-1874. [PMID: 30768902 DOI: 10.1021/acs.jctc.8b01026] [Citation(s) in RCA: 645] [Impact Index Per Article: 129.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Katarina Roos
- Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
- Department of Cell and Molecular Biology, Uppsala University, Biomedical Centre, Box 596, SE-751 24 Uppsala, Sweden
| | - Chuanjie Wu
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Wolfgang Damm
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Mark Reboul
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - James M. Stevenson
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Chao Lu
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Markus K. Dahlgren
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Sayan Mondal
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Wei Chen
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Lingle Wang
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Robert Abel
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Richard A. Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
| | - Edward D. Harder
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
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16
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Liu Y, Lu L, Zhou H, Xu F, Ma C, Huang Z, Xu J, Xu S. Chemodivergent synthesis of N-(pyridin-2-yl)amides and 3-bromoimidazo[1,2-a]pyridines from α-bromoketones and 2-aminopyridines. RSC Adv 2019; 9:34671-34676. [PMID: 35529989 PMCID: PMC9073897 DOI: 10.1039/c9ra06724h] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 10/21/2019] [Indexed: 11/21/2022] Open
Abstract
N-(Pyridin-2-yl)amides and 3-bromoimidazo[1,2-a]pyridines were synthesized respectively from α-bromoketones and 2-aminopyridine under different reaction conditions.
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Affiliation(s)
- Yanpeng Liu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry
- China Pharmaceutical University
- Nanjing 210009
- China
- Department of Chemistry
| | - Lixue Lu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry
- China Pharmaceutical University
- Nanjing 210009
- China
| | - Haipin Zhou
- College of Materials & Chemical Engineering
- Chuzhou University
- Chuzhou 239000
- China
| | - Feijie Xu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry
- China Pharmaceutical University
- Nanjing 210009
- China
| | - Cong Ma
- State Key Laboratory of Chemical Biology and Drug Discovery
- Department of Applied Biology and Chemical Technology
- The Hong Kong Polytechnic University
- Kowloon
- P. R. China
| | - Zhangjian Huang
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry
- China Pharmaceutical University
- Nanjing 210009
- China
| | - Jinyi Xu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry
- China Pharmaceutical University
- Nanjing 210009
- China
| | - Shengtao Xu
- State Key Laboratory of Natural Medicines and Department of Medicinal Chemistry
- China Pharmaceutical University
- Nanjing 210009
- China
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17
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Abstract
Accurate and reliable calculation of protein-ligand binding free energy is of central importance in computational biophysics and structure-based drug design. Among the various methods to calculate protein-ligand binding affinities, alchemical free energy perturbation (FEP) calculations performed by way of explicitly solvated molecular dynamics simulations (FEP/MD) provide a thermodynamically rigorous and complete description of the binding event and should in turn yield highly accurate predictions. Although the original theory of FEP was proposed more than 60 years ago, subsequent applications of FEP to compute protein-ligand binding free energies in the context of drug discovery projects over much of that time period was sporadic and generally unsuccessful. This was mainly due to the limited accuracy of the available force fields, inadequate sampling of the protein-ligand conformational space, complexity of simulation set up and analysis, and the large computational resources required to pursue such calculations. Over the past few years, there have been advances in computing power, classical force field accuracy, enhanced sampling algorithms, and simulation setup. This has led to newer FEP implementations such as the FEP+ technology developed by Schrödinger Inc., which has enabled accurate and reliable calculations of protein-ligand binding free energies and positioned free energy calculations to play a guiding role in small-molecule drug discovery. In this chapter, we outline the methodological advances in FEP+, including the OPLS3 force fields, the REST2 (Replica Exchange with Solute Tempering) enhanced sampling, the incorporation of REST2 sampling with conventional FEP (Free Energy Perturbation) through FEP/REST, and the advanced simulation setup and data analysis. The validation of FEP+ method in retrospective studies and the prospective applications in drug discovery projects are also discussed. We then present the recent extension of FEP+ method to handle challenging perturbations, including core-hopping transformations, macrocycle modifications, and reversible covalent inhibitor optimization. The limitations and pitfalls of the current FEP+ methodology and the best practices in real applications are also examined.
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18
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Gilabert JF, Lecina D, Estrada J, Guallar V. Monte Carlo Techniques for Drug Design: The Success Case of PELE. BIOMOLECULAR SIMULATIONS IN STRUCTURE-BASED DRUG DISCOVERY 2018. [DOI: 10.1002/9783527806836.ch5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Joan F. Gilabert
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Daniel Lecina
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Jorge Estrada
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
| | - Victor Guallar
- Barcelona Supercomputing Center (BSC); Life Science Department; Jordi Girona 29 08034 Barcelona Spain
- ICREA; Passeig Lluís Companys 23 08010 Barcelona Spain
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19
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Athanasiou C, Cournia Z. From Computers to Bedside: Computational Chemistry Contributing to FDA Approval. BIOMOLECULAR SIMULATIONS IN STRUCTURE-BASED DRUG DISCOVERY 2018. [DOI: 10.1002/9783527806836.ch7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Christina Athanasiou
- Biomedical Research Foundation; Academy of Athens; 4 Soranou Ephessiou 11527 Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation; Academy of Athens; 4 Soranou Ephessiou 11527 Athens Greece
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20
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Mobley DL, Bannan CC, Rizzi A, Bayly CI, Chodera JD, Lim VT, Lim NM, Beauchamp KA, Slochower DR, Shirts MR, Gilson MK, Eastman PK. Escaping Atom Types in Force Fields Using Direct Chemical Perception. J Chem Theory Comput 2018; 14:6076-6092. [PMID: 30351006 PMCID: PMC6245550 DOI: 10.1021/acs.jctc.8b00640] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Traditional approaches to specifying a molecular mechanics force field encode all the information needed to assign force field parameters to a given molecule into a discrete set of atom types. This is equivalent to a representation consisting of a molecular graph comprising a set of vertices, which represent atoms labeled by atom type, and unlabeled edges, which represent chemical bonds. Bond stretch, angle bend, and dihedral parameters are then assigned by looking up bonded pairs, triplets, and quartets of atom types in parameter tables to assign valence terms and using the atom types themselves to assign nonbonded parameters. This approach, which we call indirect chemical perception because it operates on the intermediate graph of atom-typed nodes, creates a number of technical problems. For example, atom types must be sufficiently complex to encode all necessary information about the molecular environment, making it difficult to extend force fields encoded this way. Atom typing also results in a proliferation of redundant parameters applied to chemically equivalent classes of valence terms, needlessly increasing force field complexity. Here, we describe a new approach to assigning force field parameters via direct chemical perception. Rather than working through the intermediary of the atom-typed graph, direct chemical perception operates directly on the unmodified chemical graph of the molecule to assign parameters. In particular, parameters are assigned to each type of force field term (e.g., bond stretch, angle bend, torsion, and Lennard-Jones) based on standard chemical substructure queries implemented via the industry-standard SMARTS chemical perception language, using SMIRKS extensions that permit labeling of specific atoms within a chemical pattern. We use this to implement a new force field format, called the SMIRKS Native Open Force Field (SMIRNOFF) format. We demonstrate the power and generality of this approach using examples of specific molecules that pose problems for indirect chemical perception and construct and validate a minimalist yet very general force field, SMIRNOFF99Frosst. We find that a parameter definition file only ∼300 lines long provides coverage of all but <0.02% of a 5 million molecule drug-like test set. Despite its simplicity, the accuracy of SMIRNOFF99Frosst for small molecule hydration free energies and selected properties of pure organic liquids is similar to that of the General Amber Force Field, whose specification requires thousands of parameters. This force field provides a starting point for further optimization and refitting work to follow.
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Affiliation(s)
- David L Mobley
- Department of Pharmaceutical Science , University of California , Irvine , California 92697 , United States
- Department of Chemistry , University of California , Irvine , California 92697 , United States
| | - Caitlin C Bannan
- Department of Chemistry , University of California , Irvine , California 92697 , United States
| | - Andrea Rizzi
- Tri-Institutional Program in Computational Biology and Medicine , New York , New York 10065 , United States
- Computational and Systems Biology Program , Memorial Sloan Kettering Cancer Center , New York , New York 10065 , United States
| | | | - John D Chodera
- Computational and Systems Biology Program , Memorial Sloan Kettering Cancer Center , New York , New York 10065 , United States
| | - Victoria T Lim
- Department of Chemistry , University of California , Irvine , California 92697 , United States
| | - Nathan M Lim
- Department of Pharmaceutical Science , University of California , Irvine , California 92697 , United States
| | - Kyle A Beauchamp
- Counsyl , South San Francisco , California 94080 , United States
| | - David R Slochower
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering , University of Colorado Boulder , Boulder , Colorado 80309 , United States
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California, San Diego , La Jolla , California 92093 , United States
| | - Peter K Eastman
- Department of Chemistry , Stanford University , Stanford , California 94305 , United States
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21
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From bench to bedside, via desktop. Recent advances in the application of cutting-edge in silico tools in the research of drugs targeting bromodomain modules. Biochem Pharmacol 2018; 159:40-51. [PMID: 30414936 DOI: 10.1016/j.bcp.2018.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Accepted: 11/07/2018] [Indexed: 11/22/2022]
Abstract
The discipline of drug discovery has greatly benefited by computational tools and in silico algorithms aiming at rationalization of many related processes, from the stage of early hit identification to the preclinical phases of drug candidate validation. The various methodologies referred to as molecular modeling tools span a broad spectrum of applications, from straightforward approaches such as virtual screening of compound libraries to more advanced techniques involving the precise estimation of free energy upon binding of the candidate drug to its macromolecular target. To this end, we report an overview of specific studies where implementation of such sophisticated modeling algorithms has shown to be indispensable for addressing challenging systems and biological questions otherwise difficult to answer. We focus our attention on the emerging field of bromodomain inhibitors. Bromodomains are small modules involved in epigenetic signaling and currently comprise high-priority targets for developing both drug candidates and chemical probes for basic biomedical research. We attempt a critical presentation of selected cases utilizing cutting-edge in silico methodologies, with our main emphasis being on absolute or relative free energy simulations, on implementation of quantum-mechanics level calculations and on characterization of solvent thermodynamics. We discuss the advantages and strengths as well as the drawbacks and weaknesses of computational tools utilized in those works and we attempt to comment on specific issues related to their integration into the regular medicinal chemistry practice. Our conclusion is that while such methods indeed represent highly promising resources for further advancing the discipline, their application is not always trivial.
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22
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Abel R, Manas ES, Friesner RA, Farid RS, Wang L. Modeling the value of predictive affinity scoring in preclinical drug discovery. Curr Opin Struct Biol 2018; 52:103-110. [PMID: 30321805 DOI: 10.1016/j.sbi.2018.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/02/2018] [Accepted: 09/07/2018] [Indexed: 12/31/2022]
Abstract
Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.
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Affiliation(s)
- Robert Abel
- Schrodinger, Inc., 120 West 45th Street, New York, NY 10036, United States.
| | - Eric S Manas
- GlaxoSmithKline, 1250 South Collegeville Road, Collegeville, PA 19426, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, New York, NY 10027, United States
| | - Ramy S Farid
- Schrodinger, Inc., 120 West 45th Street, New York, NY 10036, United States
| | - Lingle Wang
- Schrodinger, Inc., 120 West 45th Street, New York, NY 10036, United States
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23
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Hauser K, Negron C, Albanese SK, Ray S, Steinbrecher T, Abel R, Chodera JD, Wang L. Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations. Commun Biol 2018; 1:70. [PMID: 30159405 PMCID: PMC6110136 DOI: 10.1038/s42003-018-0075-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 05/15/2018] [Indexed: 12/13/2022] Open
Abstract
The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. This complicates the practice of precision medicine, pairing of patients with clinical trials, and development of next-generation inhibitors. Here, we examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). We find these calculations can achieve useful accuracy in predicting resistance for a set of eight FDA-approved kinase inhibitors across 144 clinically-identified point mutations, achieving a root mean square error in binding free energy changes of 1.1 0.9 1.3 kcal/mol (95% confidence interval) and correctly classifying mutations as resistant or susceptible with 88 82 93 % accuracy. Since these calculations are fast on modern GPUs, this benchmark establishes the potential for physical modeling to collaboratively support the rapid assessment and anticipation of the potential for patient mutations to affect drug potency in clinical applications.
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Affiliation(s)
| | | | - Steven K Albanese
- Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | | | | | | | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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24
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Iglesias J, Saen‐oon S, Soliva R, Guallar V. Computational structure‐based drug design: Predicting target flexibility. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1367] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
| | | | | | - Victor Guallar
- Life Science DepartmentBarcelonaSpain
- ICREA, Passeig Lluís Companys 23BarcelonaSpain
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25
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Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation. Sci Rep 2018; 8:4883. [PMID: 29559702 PMCID: PMC5861043 DOI: 10.1038/s41598-018-23039-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Accepted: 03/05/2018] [Indexed: 01/18/2023] Open
Abstract
A congeneric series of 21 phosphodiesterase 2 (PDE2) inhibitors are reported. Crystal structures show how the molecules can occupy a 'top-pocket' of the active site. Molecules with small substituents do not enter the pocket, a critical leucine (Leu770) is closed and water molecules are present. Large substituents enter the pocket, opening the Leu770 conformation and displacing the waters. We also report an X-ray structure revealing a new conformation of the PDE2 active site domain. The relative binding affinities of these compounds were studied with free energy perturbation (FEP) methods and it represents an attractive real-world test case. In general, the calculations could predict the energy of small-to-small, or large-to-large molecule perturbations. However, accurately capturing the transition from small-to-large proved challenging. Only when using alternative protein conformations did results improve. The new X-ray structure, along with a modelled dimer, conferred stability to the catalytic domain during the FEP molecular dynamics (MD) simulations, increasing the convergence and thereby improving the prediction of ΔΔG of binding for some small-to-large transitions. In summary, we found the most significant improvement in results when using different protein structures, and this data set is useful for future free energy validation studies.
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26
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Mena-Ulecia K, Gonzalez-Norambuena F, Vergara-Jaque A, Poblete H, Tiznado W, Caballero J. Study of the affinity between the protein kinase PKA and homoarginine-containing peptides derived from kemptide: Free energy perturbation (FEP) calculations. J Comput Chem 2018; 39:986-992. [PMID: 29399821 DOI: 10.1002/jcc.25176] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 12/05/2017] [Accepted: 01/13/2018] [Indexed: 12/28/2022]
Abstract
Protein kinases (PKs) discriminate between closely related sequences that contain serine, threonine, and/or tyrosine residues. Such specificity is defined by the amino acid sequence surrounding the phosphorylatable residue, so that it is possible to identify an optimal recognition motif (ORM) for each PK. The ORM for the protein kinase A (PKA), a well-known member of the PK family, is the sequence RRX(S/T)X, where arginines at the -3 and -2 positions play a key role with respect to the primed phosphorylation site. In this work, differential affinities of PKA for the peptide substrate Kemptide (LRRASLG) and mutants that substitute the arginine residues by the unnatural peptide homoarginine were evaluated through molecular dynamics (MD) and free energy perturbation (FEP) calculations. The FEP study for the homoarginine mutants required previous elaboration of a CHARMM "arginine to homoarginine" (R2B) hybrid topology file which is available in this manuscript as Supporting Information. Mutants substituting the arginine residues by alanine, lysine, and histidine were also considered in the comparison by using the same protocol. FEP calculations allowed estimating the free energy changes from the free PKA to PKA-substrate complex (ΔΔGE→ES ) when Kemptide structure was mutated. Both ΔΔGS→ES values for homoarginine mutants were predicted with a difference below 1 kcal/mol. In addition, FEP correctly predicted that all the studied mutations decrease the catalytic efficiency of Kemptide for PKA. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Karel Mena-Ulecia
- Departamento de Ciencias Químicas, Facultad de Ciencias Exactas, Avenida República #275, Universidad Andres Bello, Santiago de Chile, Chile
| | - Fabian Gonzalez-Norambuena
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
| | - Ariela Vergara-Jaque
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
| | - Horacio Poblete
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
| | - William Tiznado
- Departamento de Ciencias Químicas, Facultad de Ciencias Exactas, Avenida República #275, Universidad Andres Bello, Santiago de Chile, Chile
| | - Julio Caballero
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
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27
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Cournia Z, Allen B, Sherman W. Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations. J Chem Inf Model 2017; 57:2911-2937. [PMID: 29243483 DOI: 10.1021/acs.jcim.7b00564] [Citation(s) in RCA: 401] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.
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Affiliation(s)
- Zoe Cournia
- Biomedical Research Foundation, Academy of Athens , 4 Soranou Ephessiou, 11527 Athens, Greece
| | - Bryce Allen
- Silicon Therapeutics , 300 A Street, Boston, Massachusetts 02210, United States
| | - Woody Sherman
- Silicon Therapeutics , 300 A Street, Boston, Massachusetts 02210, United States
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28
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Athanasiou C, Vasilakaki S, Dellis D, Cournia Z. Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2. J Comput Aided Mol Des 2017; 32:21-44. [DOI: 10.1007/s10822-017-0075-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 10/09/2017] [Indexed: 01/21/2023]
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29
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Williams-Noonan BJ, Yuriev E, Chalmers DK. Free Energy Methods in Drug Design: Prospects of “Alchemical Perturbation” in Medicinal Chemistry. J Med Chem 2017; 61:638-649. [DOI: 10.1021/acs.jmedchem.7b00681] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Billy J. Williams-Noonan
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
| | - Elizabeth Yuriev
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
| | - David K. Chalmers
- Medicinal Chemistry, Monash
Institute of Pharmaceutical Sciences, Monash University, 381 Royal
Parade, Parkville, Victoria 3052, Australia
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30
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Abel R, Wang L, Harder ED, Berne BJ, Friesner RA. Advancing Drug Discovery through Enhanced Free Energy Calculations. Acc Chem Res 2017; 50:1625-1632. [PMID: 28677954 DOI: 10.1021/acs.accounts.7b00083] [Citation(s) in RCA: 189] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.
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Affiliation(s)
- Robert Abel
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Lingle Wang
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Edward D. Harder
- Schrödinger,
Inc., 120 West 45th Street, New York, New York 10036, United States
| | - B. J. Berne
- Department
of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
| | - Richard A. Friesner
- Department
of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States
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Abel R, Mondal S, Masse C, Greenwood J, Harriman G, Ashwell MA, Bhat S, Wester R, Frye L, Kapeller R, Friesner RA. Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr Opin Struct Biol 2017; 43:38-44. [DOI: 10.1016/j.sbi.2016.10.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 10/07/2016] [Indexed: 01/08/2023]
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Kuhn B, Tichý M, Wang L, Robinson S, Martin RE, Kuglstatter A, Benz J, Giroud M, Schirmeister T, Abel R, Diederich F, Hert J. Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors. J Med Chem 2017; 60:2485-2497. [PMID: 28287264 DOI: 10.1021/acs.jmedchem.6b01881] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Improving the binding affinity of a chemical series by systematically probing one of its exit vectors is a medicinal chemistry activity that can benefit from molecular modeling input. Herein, we compare the effectiveness of four approaches in prioritizing building blocks with better potency: selection by a medicinal chemist, manual modeling, docking followed by manual filtering, and free energy calculations (FEP). Our study focused on identifying novel substituents for the apolar S2 pocket of cathepsin L and was conducted entirely in a prospective manner with synthesis and activity determination of 36 novel compounds. We found that FEP selected compounds with improved affinity for 8 out of 10 picks compared to 1 out of 10 for the other approaches. From this result and other additional analyses, we conclude that FEP can be a useful approach to guide this type of medicinal chemistry optimization once it has been validated for the system under consideration.
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Affiliation(s)
- Bernd Kuhn
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. , Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Michal Tichý
- Laboratorium für Organische Chemie, ETH Zurich , Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland
| | - Lingle Wang
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - Shaughnessy Robinson
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - Rainer E Martin
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. , Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Andreas Kuglstatter
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. , Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Jörg Benz
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. , Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Maude Giroud
- Laboratorium für Organische Chemie, ETH Zurich , Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland
| | - Tanja Schirmeister
- Institut für Pharmazie und Biochemie, Johannes Gutenberg-Universität Mainz , Staudinger Weg 5, 55128 Mainz, Germany
| | - Robert Abel
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - François Diederich
- Laboratorium für Organische Chemie, ETH Zurich , Vladimir-Prelog-Weg 3, 8093 Zurich, Switzerland
| | - Jérôme Hert
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. , Grenzacherstrasse 124, 4070 Basel, Switzerland
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33
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Keränen H, Pérez-Benito L, Ciordia M, Delgado F, Steinbrecher TB, Oehlrich D, van Vlijmen HWT, Trabanco AA, Tresadern G. Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study. J Chem Theory Comput 2017; 13:1439-1453. [PMID: 28103438 DOI: 10.1021/acs.jctc.6b01141] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
A series of acylguanidine beta secretase 1 (BACE1) inhibitors with modified scaffold and P3 pocket substituent was synthesized and studied with free energy perturbation (FEP) calculations. The resulting molecules showed potencies in enzymatic BACE1 inhibition assays up to 1 nM. The correlation between the predicted activity from the FEP calculations and the experimental activity was good for the P3 pocket substituents. The average mean unsigned error (MUE) between prediction and experiment was 0.68 ± 0.17 kcal/mol for the default 5 ns lambda window simulation time improving to 0.35 ± 0.13 kcal/mol for 40 ns. FEP calculations for the P2' pocket substituents on the same acylguanidine scaffold also showed good agreement with experiment and the results remained stable with repeated simulations and increased simulation time. It proved more difficult to use FEP calculations to study the scaffold modification from increasing 5 to 6 and 7 membered-rings. Although prediction and experiment were in agreement for short 2 ns simulations, as the simulation time increased the results diverged. This was improved by the use of a newly developed "Core Hopping FEP+" approach, which also showed improved stability in repeat calculations. The origins of these differences along with the value of repeat and longer simulation times are discussed. This work provides a further example of the use of FEP as a computational tool for molecular design.
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Affiliation(s)
- Henrik Keränen
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. , Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Laura Pérez-Benito
- Laboratori de Medicina Computacional Unitat de Bioestadistica, Facultat de Medicina, Universitat Autonoma de Barcelona , 08193, Bellaterra, Spain.,Computational Chemistry, Janssen Research and Development, Janssen-Cilag , c/ Jarama 75A, 45007, Toledo, Spain
| | - Myriam Ciordia
- Neuroscience Medicinal Chemistry, Janssen Research and Development, Janssen-Cilag , c/ Jarama 75A, 45007, Toledo, Spain
| | - Francisca Delgado
- Neuroscience Medicinal Chemistry, Janssen Research and Development, Janssen-Cilag , c/ Jarama 75A, 45007, Toledo, Spain
| | | | - Daniel Oehlrich
- Neuroscience Medicinal Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. , Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Herman W T van Vlijmen
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. , Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Andrés A Trabanco
- Neuroscience Medicinal Chemistry, Janssen Research and Development, Janssen-Cilag , c/ Jarama 75A, 45007, Toledo, Spain
| | - Gary Tresadern
- Computational Chemistry, Janssen Research and Development, Janssen-Cilag , c/ Jarama 75A, 45007, Toledo, Spain
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34
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Ciordia M, Pérez-Benito L, Delgado F, Trabanco AA, Tresadern G. Application of Free Energy Perturbation for the Design of BACE1 Inhibitors. J Chem Inf Model 2016; 56:1856-71. [PMID: 27500414 DOI: 10.1021/acs.jcim.6b00220] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Novel spiroaminodihydropyrroles probing for optimized interactions at the P3 pocket of β-secretase 1 (BACE1) were designed with the use of free energy perturbation (FEP) calculations. The resulting molecules showed pIC50 potencies in enzymatic BACE1 inhibition assays ranging from approximately 5 to 7. Good correlation was observed between the predicted activity from the FEP calculations and experimental activity. Simulations run with a default 5 ns approach delivered a mean unsigned error (MUE) between prediction and experiment of 0.58 and 0.91 kcal/mol for retrospective and prospective applications, respectively. With longer simulations of 10 and 20 ns, the MUE was in both cases 0.57 kcal/mol for the retrospective application, and 0.69 and 0.59 kcal/mol for the prospective application. Other considerations that impact the quality of the calculations are discussed. This work provides an example of the value of FEP as a computational tool for drug discovery.
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Affiliation(s)
- Myriam Ciordia
- Janssen Research and Development , c/Jarama 75A, 45007 Toledo, Spain.,Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad CEU San Pablo , Urbanización Montepríncipe Ctra., Boadilla del Monte Km 5.3, 28668 Madrid, Spain
| | - Laura Pérez-Benito
- Janssen Research and Development , c/Jarama 75A, 45007 Toledo, Spain.,Laboratori de Medicina Computacional Unitat de Bioestadistica, Facultat de Medicina, Universitat Autonoma de Barcelona , 08193 Bellaterra, Spain
| | - Francisca Delgado
- Janssen Research and Development , c/Jarama 75A, 45007 Toledo, Spain
| | - Andrés A Trabanco
- Janssen Research and Development , c/Jarama 75A, 45007 Toledo, Spain
| | - Gary Tresadern
- Janssen Research and Development , c/Jarama 75A, 45007 Toledo, Spain
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35
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The importance of protonation and tautomerization in relative binding affinity prediction: a comparison of AMBER TI and Schrödinger FEP. J Comput Aided Mol Des 2016; 30:533-9. [PMID: 27480697 DOI: 10.1007/s10822-016-9920-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 06/24/2016] [Indexed: 10/21/2022]
Abstract
In drug discovery, protonation states and tautomerization are easily overlooked. Through a Merck-Rutgers collaboration, this paper re-examined the initial settings and preparations for the Thermodynamic Integration (TI) calculation in AMBER Free-Energy Workflows, demonstrating the value of careful consideration of ligand protonation and tautomer state. Finally, promising results comparing AMBER TI and Schrödinger FEP+ are shown that should encourage others to explore the value of TI in routine Structure-based Drug Design.
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