1
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Wang Z, Wang S, Wang H, Hu B, Qi Z, Zhang Y, Song P, Cai Q, Yang H, Wang J. Uncovering the selectivity mechanism of phosphodiesterase 7A/8A inhibitors through computational studies. Phys Chem Chem Phys 2024; 26:11770-11781. [PMID: 38566586 DOI: 10.1039/d3cp03913g] [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: 04/04/2024]
Abstract
The expression of phosphodiesterase 7A (PDE7A) and phosphodiesterase 8A (PDE8) genes is integral to human signaling pathways, and the inhibition of PDE7A has been associated with the onset of various diseases, including effects on the immune system and nervous system. The development of PDE7 selective inhibitors can promote research on immune and nervous system diseases, such as multiple sclerosis, chronic inflammation, and autoimmune responses. PDE8A is expressed alongside PDE8B, and its inhibitory mechanism is still unclear. Studying the mechanisms of selective inhibitors against different PDE subtypes is crucial to prevent potential side effects, such as nausea and cardiac toxicity, and the sequence similarity of the two protein subtypes was 55.9%. Therefore, it is necessary to investigate the differences of both subtypes' ligand binding sites. Selective inhibitors of two proteins were chosen to summarize the reason for their selectivity through molecular docking, molecular dynamics simulation, alanine scanning mutagenesis, and MM-GBSA calculation. We found that Phe384PDE7A, Leu401PDE7A, Gln413PDE7A, Tyr419PDE7A, and Phe416PDE7A in the active site positively contribute to the selectivity towards PDE7A. Additionally, Asn729PDE8A, Phe767PDE8A, Gln778PDE8A, and Phe781PDE8A positively contribute to the selectivity towards PDE8A.
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Affiliation(s)
- Zhijian Wang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Shizun Wang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Hanxun Wang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Baichun Hu
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Zhuo Qi
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Yaming Zhang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Pengfei Song
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Qingkui Cai
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Huali Yang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Structure-Based Drug Design and Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jian Wang
- School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Structure-Based Drug Design and Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
- Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, China
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2
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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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3
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Diao Y, Liu D, Ge H, Zhang R, Jiang K, Bao R, Zhu X, Bi H, Liao W, Chen Z, Zhang K, Wang R, Zhu L, Zhao Z, Hu Q, Li H. Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery. Nat Commun 2023; 14:4552. [PMID: 37507402 PMCID: PMC10382584 DOI: 10.1038/s41467-023-40219-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors.
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Affiliation(s)
- Yanyan Diao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Dandan Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Huan Ge
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Rongrong Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Kexin Jiang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Runhui Bao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Xiaoqian Zhu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Hongjie Bi
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Wenjie Liao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Ziqi Chen
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Kai Zhang
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China
| | - Rui Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Lili Zhu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Zhenjiang Zhao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China
| | - Qiaoyu Hu
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai, 200237, China.
- Innovation Center for AI and Drug Discovery, East China Normal University, Shanghai, 200062, China.
- Lingang Laboratory, Shanghai, 200031, China.
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4
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Wu Y, Wang S, Wang H, Hu B, Wang J. Selectivity mechanism of GRK2/5 inhibition through in silico investigation. Comput Biol Chem 2022; 101:107786. [DOI: 10.1016/j.compbiolchem.2022.107786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/02/2022] [Accepted: 11/05/2022] [Indexed: 11/09/2022]
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5
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Hahn DF, Bayly CI, Boby ML, Macdonald HEB, Chodera JD, Gapsys V, Mey ASJS, Mobley DL, Benito LP, Schindler CEM, Tresadern G, Warren GL. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1497. [PMID: 36382113 PMCID: PMC9662604 DOI: 10.33011/livecoms.4.1.1497] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.
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Affiliation(s)
- David F. Hahn
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Melissa L. Boby
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
- MSD R&D Innovation Centre, 120 Moorgate, London EC2M 6UR, United Kingdom
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, CA USA
| | - Laura Perez Benito
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | | | - Gary Tresadern
- Computational Chemistry,Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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6
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Masoodi HR, Bagheri S, Gholipour A, Rohani Moghadam M, Bazmandegan-Shamili A. DFT study of stability and electronic properties of cyclic tetramer involving dinucleobase monomers, comprising acetylene central block substituted at both edges with guanine and cytosine nucleobases. Mol Phys 2022. [DOI: 10.1080/00268976.2022.2096141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Hamid Reza Masoodi
- Department of Chemistry, Faculty of Science, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Sotoodeh Bagheri
- Department of Chemistry, Faculty of Science, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
| | - Alireza Gholipour
- Department of Chemistry, Faculty of Science, Lorestan University, Khoramabad, Iran
| | - Masoud Rohani Moghadam
- Department of Chemistry, Faculty of Science, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran
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7
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Morawietz T, Artrith N. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications. J Comput Aided Mol Des 2021; 35:557-586. [PMID: 33034008 PMCID: PMC8018928 DOI: 10.1007/s10822-020-00346-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/26/2020] [Indexed: 01/13/2023]
Abstract
Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.
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Affiliation(s)
- Tobias Morawietz
- Bayer AG, Pharmaceuticals, R&D, Digital Technologies, Computational Molecular Design, 42096 Wuppertal, Germany
| | - Nongnuch Artrith
- Department of Chemical Engineering, Columbia University, New York, NY 10027 USA
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8
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Wang H, Wang Y, Li C, Wang H, Geng X, Hu B, Wen R, Wang J, Zhang F. Structural basis for tailor-made selective PI3K α/β inhibitors: a computational perspective. NEW J CHEM 2021. [DOI: 10.1039/d0nj04216a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PI3K α and β are Class IA PI3K isoforms that share a highly homologous ATP binding site, differing only in a few residues around the binding site.
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Affiliation(s)
- Huibin Wang
- School of Pharmacy
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
| | - Ying Wang
- Wuya College of Innovation
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
| | - Chunshi Li
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
- School of Pharmaceutical Engineering
| | - Hanxun Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
- School of Pharmaceutical Engineering
| | - Xiaohui Geng
- School of Pharmacy
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
- School of Pharmaceutical Engineering
| | - Rui Wen
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
- School of Pharmaceutical Engineering
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design & Discovery of Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
- School of Pharmaceutical Engineering
| | - Fengjiao Zhang
- Wuya College of Innovation
- Shenyang Pharmaceutical University
- Shenyang 110016
- People's Republic of China
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9
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Bluntzer MTJ, O'Connell J, Baker TS, Michel J, Hulme AN. Designing stapled peptides to inhibit
protein‐protein
interactions: An analysis of successes in a rapidly changing field. Pept Sci (Hoboken) 2020. [DOI: 10.1002/pep2.24191] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
| | | | | | - Julien Michel
- EaStChem School of Chemistry The University of Edinburgh Edinburgh UK
| | - Alison N. Hulme
- EaStChem School of Chemistry The University of Edinburgh Edinburgh UK
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10
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Sindhikara D, Wagner M, Gkeka P, Güssregen S, Tiwari G, Hessler G, Yapici E, Li Z, Evers A. Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins. J Med Chem 2020; 63:12100-12115. [PMID: 33017535 DOI: 10.1021/acs.jmedchem.0c01500] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Macrocycles and cyclic peptides are increasingly attractive therapeutic modalities as they often have improved affinity, are able to bind to extended protein surfaces, and otherwise have favorable properties. Macrocyclization of a known binder may stabilize its bioactive conformation and improve its metabolic stability, cell permeability, and in certain cases oral bioavailability. Herein, we present implementation and application of an approach that automatically generates, evaluates, and proposes cyclizations utilizing a library of well-established chemical reactions and reagents. Using the three-dimensional (3D) conformation of the linear molecule in complex with a target protein as the starting point, this approach identifies attachment points, generates linkers, evaluates their geometric compatibility, and ranks the resulting molecules with respect to their predicted conformational stability and interactions with the target protein. As we show here with prospective and retrospective case studies, this procedure can be applied for the macrocyclization of small molecules and peptides and even PROteolysis TArgeting Chimeras (PROTACs) and proteins.
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Affiliation(s)
- Dan Sindhikara
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Michael Wagner
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
| | - Paraskevi Gkeka
- Integrated Drug Discovery, Sanofi R&D, 1 Avenue Pierre Brossolette, 91385 Chilly-Mazarin, France
| | - Stefan Güssregen
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
| | - Garima Tiwari
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
| | - Gerhard Hessler
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
| | - Engin Yapici
- Schrodinger, Inc., 120 West 45th Street, New York, New York 10036, United States
| | - Ziyu Li
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
| | - Andreas Evers
- Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
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11
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Deflorian F, Perez-Benito L, Lenselink EB, Congreve M, van Vlijmen HWT, Mason JS, Graaf CD, Tresadern G. Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation. J Chem Inf Model 2020; 60:5563-5579. [PMID: 32539374 DOI: 10.1021/acs.jcim.0c00449] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R2) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.
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Affiliation(s)
- Francesca Deflorian
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG United Kingdom
| | - Laura Perez-Benito
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Eelke B Lenselink
- Division of Medicinal Chemistry, Leiden Academic Centre for Drug Research, Leiden University, Leiden 2300, RA, The Netherlands
| | - Miles Congreve
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG United Kingdom
| | - Herman W T van Vlijmen
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Jonathan S Mason
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG United Kingdom
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge CB21 6DG United Kingdom
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium
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12
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Paulsen JL, Yu HS, Sindhikara D, Wang L, Appleby T, Villaseñor AG, Schmitz U, Shivakumar D. Evaluation of Free Energy Calculations for the Prioritization of Macrocycle Synthesis. J Chem Inf Model 2020; 60:3489-3498. [DOI: 10.1021/acs.jcim.0c00132] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Janet L. Paulsen
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Haoyu S. Yu
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Dan Sindhikara
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Lingle Wang
- Schrödinger, Inc., 120 West 45th Street, 17th Floor, New York, New York 10036, United States
| | - Todd Appleby
- Gilead, 333 Lakeside Drive, Foster City, California 94404, United States
| | | | - Uli Schmitz
- Gilead, 333 Lakeside Drive, Foster City, California 94404, United States
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13
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Duan J, Lupyan D, Wang L. Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations. Biophys J 2020; 119:115-127. [PMID: 32533939 DOI: 10.1016/j.bpj.2020.05.020] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/30/2020] [Accepted: 05/18/2020] [Indexed: 01/17/2023] Open
Abstract
Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.
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14
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Wallraven K, Holmelin FL, Glas A, Hennig S, Frolov AI, Grossmann TN. Adapting free energy perturbation simulations for large macrocyclic ligands: how to dissect contributions from direct binding and free ligand flexibility. Chem Sci 2020; 11:2269-2276. [PMID: 32180932 PMCID: PMC7057854 DOI: 10.1039/c9sc04705k] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 01/20/2020] [Indexed: 11/25/2022] Open
Abstract
Large and flexible ligands gain increasing interest in the development of bioactive agents. They challenge the applicability of computational ligand optimization strategies originally developed for small molecules. Free energy perturbation (FEP) is often used for predicting binding affinities of small molecule ligands, however, its use for more complex ligands remains limited. Herein, we report the structure-based design of peptide macrocycles targeting the protein binding site of human adaptor protein 14-3-3. We observe a surprisingly strong dependency of binding affinities on relatively small variations in substituent size. FEP was performed to rationalize observed trends. To account for insufficient convergence of FEP, restrained calculations were performed and complemented with extensive REST MD simulations of the free ligands. These calculations revealed that changes in affinity originate both from altered direct interactions and conformational changes of the free ligand. In addition, MD simulations provided the basis to rationalize unexpected trends in ligand lipophilicity. We also verified the anticipated interaction site and binding mode for one of the high affinity ligands by X-ray crystallography. The introduced fully-atomistic simulation protocol can be used to rationalize the development of structurally complex ligands which will support future ligand maturation efforts.
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Affiliation(s)
- Kerstin Wallraven
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
| | - Fredrik L Holmelin
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism , BioPharmaceuticals R&D , AstraZeneca , Pepparedsleden 1, Mölndal , 431 83 , Sweden .
| | - Adrian Glas
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
| | - Sven Hennig
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
| | - Andrey I Frolov
- Medicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism , BioPharmaceuticals R&D , AstraZeneca , Pepparedsleden 1, Mölndal , 431 83 , Sweden .
| | - Tom N Grossmann
- Department of Chemistry & Pharmaceutical Sciences , VU University Amsterdam , De Boelelaan 1083 , 1081 HV Amsterdam , The Netherlands .
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15
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Mey ASJS, Allen BK, Macdonald HEB, Chodera JD, Hahn DF, Kuhn M, Michel J, Mobley DL, Naden LN, Prasad S, Rizzi A, Scheen J, Shirts MR, Tresadern G, Xu H. Best Practices for Alchemical Free Energy Calculations [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2020; 2:18378. [PMID: 34458687 PMCID: PMC8388617 DOI: 10.33011/livecoms.2.1.18378] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.
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Affiliation(s)
- Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Hannah E. Bruce Macdonald
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York NY, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Maximilian Kuhn
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
- Cresset, Cambridgeshire, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California, Irvine, Irvine, USA
| | - Levi N. Naden
- Molecular Sciences Software Institute, Blacksburg VA, USA
| | | | - Andrea Rizzi
- Silicon Therapeutics, Boston, MA, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Jenke Scheen
- EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King’s Buildings, Edinburgh, EH9 3FJ, UK
| | | | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, Beerse B-2340, Belgium
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16
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Gapsys V, Pérez-Benito L, Aldeghi M, Seeliger D, van Vlijmen H, Tresadern G, de Groot BL. Large scale relative protein ligand binding affinities using non-equilibrium alchemy. Chem Sci 2019; 11:1140-1152. [PMID: 34084371 PMCID: PMC8145179 DOI: 10.1039/c9sc03754c] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 12/01/2019] [Indexed: 12/14/2022] Open
Abstract
Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.
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Affiliation(s)
- Vytautas Gapsys
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
| | - Laura Pérez-Benito
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 B-2340 Beerse Belgium
| | - Matteo Aldeghi
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
| | - Daniel Seeliger
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG Birkendorfer Strasse 65 D-88397 Biberach a.d. Riss Germany
| | - Herman van Vlijmen
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 B-2340 Beerse Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Janssen Pharmaceutica N. V. Turnhoutseweg 30 B-2340 Beerse Belgium
| | - Bert L de Groot
- Computational Biomolecular Dynamics Group, Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry D-37077 Göttingen Germany
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17
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Yoshikawa N, Hutchison GR. Fast, efficient fragment-based coordinate generation for Open Babel. J Cheminform 2019; 11:49. [PMID: 31372768 PMCID: PMC6676618 DOI: 10.1186/s13321-019-0372-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 07/23/2019] [Indexed: 12/19/2022] Open
Abstract
Rapidly predicting an accurate three dimensional geometry of a molecule is a crucial task for cheminformatics and across a wide range of molecular modeling. Consequently, developing a fast, accurate, and open implementation of structure prediction is necessary for reproducible cheminformatics research. We introduce a fragment-based coordinate generation implementation for Open Babel, a widely-used open source toolkit for cheminformatics. The new implementation improves speed and stereochemical accuracy, while retaining or improving accuracy of bond lengths, bond angles, and dihedral torsions. Input molecules are broken into fragments by cutting at rotatable bonds. The coordinates of fragments are set according to a fragment library, prepared from open crystallographic databases. Since the coordinates of multiple atoms are decided at once, coordinate prediction is accelerated over the previous rules-based implementation in Open Babel, as well as the widely-used distance geometry methods in RDKit. This new implementation will be beneficial for a wide range of applications, including computational property prediction in polymers, molecular materials and drug design.
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Affiliation(s)
- Naruki Yoshikawa
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Geoffrey R Hutchison
- Department of Chemistry and Chemical Engineering, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, PA, 15260, USA.
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18
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Cummings MD, Sekharan S. Structure-Based Macrocycle Design in Small-Molecule Drug Discovery and Simple Metrics To Identify Opportunities for Macrocyclization of Small-Molecule Ligands. J Med Chem 2019; 62:6843-6853. [DOI: 10.1021/acs.jmedchem.8b01985] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Maxwell D. Cummings
- Janssen Research and Development, LLC, Welsh and McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Sivakumar Sekharan
- Cambridge Crystallographic Data Centre, 252 Nassau Street, Princeton, New Jersey 08542, United States
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19
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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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20
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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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21
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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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22
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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: 39] [Impact Index Per Article: 6.5] [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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23
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Wagner V, Jantz L, Briem H, Sommer K, Rarey M, Christ CD. Computational Macrocyclization: From de novo Macrocycle Generation to Binding Affinity Estimation. ChemMedChem 2017; 12:1866-1872. [PMID: 28977738 PMCID: PMC5725703 DOI: 10.1002/cmdc.201700478] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 09/26/2017] [Indexed: 12/21/2022]
Abstract
Macrocycles play an increasing role in drug discovery, but their synthesis is often demanding. Computational tools that suggest macrocyclization based on a known binding mode and that estimate the binding affinity of these macrocycles could have a substantial impact on the medicinal chemistry design process. For both tasks, we established a workflow with high practical value. For five diverse pharmaceutical targets we show that the effect of macrocyclization on binding can be calculated robustly and accurately. Applying this method to macrocycles designed by LigMac, a search tool for de novo macrocyclization, our results suggest that we have a robust protocol in hand to design macrocycles and prioritize them prior to synthesis.
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Affiliation(s)
- Vincent Wagner
- Bayer AG, Drug Discovery, Medicinal Chemistry13353BerlinGermany
| | - Linda Jantz
- Universität HamburgZBH-Center for Bioinformatics20146HamburgGermany
| | - Hans Briem
- Bayer AG, Drug Discovery, Medicinal Chemistry13353BerlinGermany
| | - Kai Sommer
- Universität HamburgZBH-Center for Bioinformatics20146HamburgGermany
| | - Matthias Rarey
- Universität HamburgZBH-Center for Bioinformatics20146HamburgGermany
| | - Clara D. Christ
- Bayer AG, Drug Discovery, Medicinal Chemistry13353BerlinGermany
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