1
|
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: 24] [Impact Index Per Article: 24.0] [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.
Collapse
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
| |
Collapse
|
2
|
D’Amore L, Hahn DF, Dotson DL, Horton JT, Anwar J, Craig I, Fox T, Gobbi A, Lakkaraju SK, Lucas X, Meier K, Mobley DL, Narayanan A, Schindler CE, Swope WC, in ’t Veld PJ, Wagner J, Xue B, Tresadern G. Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field. J Chem Inf Model 2022; 62:6094-6104. [PMID: 36433835 PMCID: PMC9873353 DOI: 10.1021/acs.jcim.2c01185] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.
Collapse
Affiliation(s)
- Lorenzo D’Amore
- Computational Chemistry, Janssen R&D, C/ Jarama 75A, 45007 Toledo, Spain
| | - David F. Hahn
- Computational Chemistry, Janssen R&D, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - David L. Dotson
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, California 95616, USA
| | - Joshua T. Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Jamshed Anwar
- Department of Chemistry, Lancaster University, Lancaster LA1 4YW, UK
| | - Ian Craig
- Molecular Modeling & Drug Discovery, BASF SE, 67056 Ludwigshafen, Germany
| | - Thomas Fox
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397 Biberach/Riss, Germany
| | - Alberto Gobbi
- Genentech, Inc., 1 DNA Way, South San Francisco, California, 94080, USA
| | | | - Xavier Lucas
- Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Katharina Meier
- Computational Life Science Technology Functions, Crop Science, R&D, Bayer AG, 40789 Monheim, Germany
| | - David L. Mobley
- Departments of Pharmaceutical Sciences and Chemistry, University of California 92617, Irvine, USA
| | - Arjun Narayanan
- Data and Computational Sciences, Vertex Pharmaceuticals, 50 Northern Ave, Boston, MA 02210, USA
| | | | - William C. Swope
- Genentech, Inc., 1 DNA Way, South San Francisco, California, 94080, USA
| | | | - Jeffrey Wagner
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, California, 95616, USA,Chemistry Department, The University of California at Irvine, Irvine, California, 92617, USA
| | - Bai Xue
- XtalPi Inc. Floor 3, International Biomedical Innovation Park II, No. 2 Hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, Guangdong, 518040 China
| | - Gary Tresadern
- Computational Chemistry, Janssen R&D, Turnhoutseweg 30, Beerse B-2340, Belgium
| |
Collapse
|
3
|
Abstract
Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.
Collapse
Affiliation(s)
- Serge Perez
- Centre de Recherche sur les Macromolecules Vegetales, University of Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble F-38041, France
| | - Olga Makshakova
- FRC Kazan Scientific Center of Russian Academy of Sciences, Kazan Institute of Biochemistry and Biophysics, Kazan 420111, Russia
| |
Collapse
|
4
|
Zhang Q, Yang Y, Gong X, Zhao N, Zhang Y, Liu H. Thermodynamic integration combined with molecular dynamic simulations to explore the
cross‐resistance
mechanism of isoniazid and ethionamide. Proteins 2022; 90:1142-1151. [DOI: 10.1002/prot.26295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 12/25/2021] [Accepted: 12/30/2021] [Indexed: 11/10/2022]
Affiliation(s)
| | - Yuwei Yang
- School of Pharmacy Lanzhou University Lanzhou China
| | - Xiaoqing Gong
- State Key Laboratory of Applied Organic Chemistry and Department of Chemistry Lanzhou University Lanzhou China
| | - Nannan Zhao
- School of Pharmacy Lanzhou University Lanzhou China
| | - Yang Zhang
- Supercomputing Center of Lanzhou University Lanzhou China
| | | |
Collapse
|
5
|
Gao M, Li H, Ye C, Chen K, Jiang H, Yu K. Glycan Epitopes and Potential Glycoside Antagonists of DC-SIGN Involved in COVID-19: In Silico Study. Biomolecules 2021; 11:1586. [PMID: 34827585 PMCID: PMC8615854 DOI: 10.3390/biom11111586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 12/28/2022] Open
Abstract
Glycosylation is an important post-translational modification that affects a wide variety of physiological functions. DC-SIGN (Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin) is a protein expressed in antigen-presenting cells that recognizes a variety of glycan epitopes. Until now, the binding of DC-SIGN to SARS-CoV-2 Spike glycoprotein has been reported in various articles and is regarded to be a factor in systemic infection and cytokine storm. The mechanism of DC-SIGN recognition offers an alternative method for discovering new medication for COVID-19 treatment. Here, we discovered three potential pockets that hold different glycan epitopes by performing molecular dynamics simulations of previously reported oligosaccharides. The "EPN" motif, "NDD" motif, and Glu354 form the most critical pocket, which is known as the Core site. We proposed that the type of glycan epitopes, rather than the precise amino acid sequence, determines the recognition. Furthermore, we deduced that oligosaccharides could occupy an additional site, which adds to their higher affinity than monosaccharides. Based on our findings and previously described glycoforms on the SARS-CoV-2 Spike, we predicted the potential glycan epitopes for DC-SIGN. It suggested that glycan epitopes could be recognized at multiple sites, not just Asn234, Asn149 and Asn343. Subsequently, we found that Saikosaponin A and Liquiritin, two plant glycosides, were promising DC-SIGN antagonists in silico.
Collapse
Affiliation(s)
- Meina Gao
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; (M.G.); (K.C.); (H.J.)
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200031, China;
| | - Hui Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200031, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Chenghao Ye
- Department of Chemistry, Shantou University, Shantou 515063, China;
| | - Kaixian Chen
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; (M.G.); (K.C.); (H.J.)
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200031, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hualiang Jiang
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing 210023, China; (M.G.); (K.C.); (H.J.)
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200031, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Kunqian Yu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200031, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
6
|
Nelson L, Bariami S, Ringrose C, Horton JT, Kurdekar V, Mey ASJS, Michel J, Cole DJ. Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations. J Chem Inf Model 2021; 61:2124-2130. [PMID: 33886305 DOI: 10.1021/acs.jcim.1c00328] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The quantum mechanical bespoke (QUBE) force-field approach has been developed to facilitate the automated derivation of potential energy function parameters for modeling protein-ligand binding. To date, the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free-energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of graphics processing unit acceleration will facilitate high-throughput alchemical free-energy calculations.
Collapse
Affiliation(s)
- Lauren Nelson
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Sofia Bariami
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Chris Ringrose
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Joshua T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Vadiraj Kurdekar
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| |
Collapse
|
7
|
Lim VT, Hahn DF, Tresadern G, Bayly CI, Mobley DL. Benchmark assessment of molecular geometries and energies from small molecule force fields. F1000Res 2020; 9:Chem Inf Sci-1390. [PMID: 33604023 PMCID: PMC7863993 DOI: 10.12688/f1000research.27141.1] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/18/2020] [Indexed: 12/22/2022] Open
Abstract
Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.
Collapse
Affiliation(s)
- Victoria T. Lim
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
| | - David F. Hahn
- Computational Chemistry, Janssen Research & Development, Beerse, B-2340, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Research & Development, Beerse, B-2340, Belgium
| | | | - David L. Mobley
- Department of Chemistry, University of California, Irvine, CA, 92697, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| |
Collapse
|
8
|
Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
Collapse
Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
| |
Collapse
|
9
|
Scheen J, Wu W, Mey ASJS, Tosco P, Mackey M, Michel J. Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies. J Chem Inf Model 2020; 60:5331-5339. [PMID: 32639733 DOI: 10.1021/acs.jcim.0c00600] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations and to flag molecules which will benefit the most from bespoke force field parametrization efforts.
Collapse
Affiliation(s)
- Jenke Scheen
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Wilson Wu
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| | - Paolo Tosco
- Cresset Group, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire SG8 0SS, United Kingdom
| | - Mark Mackey
- Cresset Group, New Cambridge House, Bassingbourn Road, Litlington, Cambridgeshire SG8 0SS, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, United Kingdom
| |
Collapse
|
10
|
Kuhn M, Firth-Clark S, Tosco P, Mey ASJS, Mackey M, Michel J. Assessment of Binding Affinity via Alchemical Free-Energy Calculations. J Chem Inf Model 2020; 60:3120-3130. [DOI: 10.1021/acs.jcim.0c00165] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Maximilian Kuhn
- Cresset, New Cambridge House, Bassingbourn Road, Litlington SG8 0SS, Cambridgeshire, U.K
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, U.K
| | - Stuart Firth-Clark
- Cresset, New Cambridge House, Bassingbourn Road, Litlington SG8 0SS, Cambridgeshire, U.K
| | - Paolo Tosco
- Cresset, New Cambridge House, Bassingbourn Road, Litlington SG8 0SS, Cambridgeshire, U.K
| | - Antonia S. J. S. Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, U.K
| | - Mark Mackey
- Cresset, New Cambridge House, Bassingbourn Road, Litlington SG8 0SS, Cambridgeshire, U.K
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, U.K
| |
Collapse
|
11
|
Granadino-Roldán JM, Mey ASJS, Pérez González JJ, Bosisio S, Rubio-Martinez J, Michel J. Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors. PLoS One 2019; 14:e0213217. [PMID: 30861030 PMCID: PMC6413950 DOI: 10.1371/journal.pone.0213217] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 02/15/2019] [Indexed: 11/19/2022] Open
Abstract
Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking–MM/PBSA–AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.
Collapse
Affiliation(s)
- José M. Granadino-Roldán
- Departamento de Química Física, Facultad de Ciencias Experimentales, Universidad de Jaén, Campus “Las Lagunillas” s/n, Jaén, Spain
- * E-mail: (JMG); (JM)
| | | | - Juan J. Pérez González
- Department of Chemical Engineering, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Stefano Bosisio
- EaStCHEM School of Chemistry, Joseph Black Building, Edinburgh, United Kingdom
| | - Jaime Rubio-Martinez
- Departament de Química Física, Universitat de Barcelona (UB) and the Institut de Recerca en Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, Barcelona, Spain
| | - Julien Michel
- EaStCHEM School of Chemistry, Joseph Black Building, Edinburgh, United Kingdom
- * E-mail: (JMG); (JM)
| |
Collapse
|
12
|
Zanette C, Bannan CC, Bayly CI, Fass J, Gilson MK, Shirts MR, Chodera JD, Mobley DL. Toward Learned Chemical Perception of Force Field Typing Rules. J Chem Theory Comput 2019; 15:402-423. [PMID: 30512951 PMCID: PMC6467725 DOI: 10.1021/acs.jctc.8b00821] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular mechanics force fields define how the energy and forces in a molecular system are computed from its atomic positions, thus enabling the study of such systems through computational methods like molecular dynamics and Monte Carlo simulations. Despite progress toward automated force field parametrization, considerable human expertise is required to develop or extend force fields. In particular, human input has long been required to define atom types, which encode chemically unique environments that determine which parameters will be assigned. However, relying on humans to establish atom types is suboptimal. Human-created atom types are often developed without statistical justification, leading to over- or under-fitting of data. Human-created types are also difficult to extend in a systematic and consistent manner when new chemistries must be modeled or new data becomes available. Finally, human effort is not scalable when force fields must be generated for new (bio)polymers, compound classes, or materials. To remedy these deficiencies, our long-term goal is to replace human specification of atom types with an automated approach, based on rigorous statistics and driven by experimental and/or quantum chemical reference data. In this work, we describe novel methods that automate the discovery of appropriate chemical perception: SMARTY allows for the creation of atom types, while SMIRKY goes further by automating the creation of fragment (nonbonded, bonds, angles, and torsions) types. These approaches enable the creation of move sets in atom or fragment type space, which are used within a Monte Carlo optimization approach. We demonstrate the power of these new methods by automating the rediscovery of human defined atom types (SMARTY) or fragment types (SMIRKY) in existing small molecule force fields. We assess these approaches using several molecular data sets, including one which covers a diverse subset of the DrugBank database.
Collapse
Affiliation(s)
- Camila Zanette
- Department of Pharmaceutical Sciences, University of California, Irvine
| | | | | | - Josh Fass
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Michael K. Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego
| | - Michael R. Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309
| | - John D. Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - David L. Mobley
- Department of Pharmaceutical Sciences, University of California, Irvine
- Department of Chemistry, University of California, Irvine
| |
Collapse
|
13
|
Mishra SK, Koča J. Assessing the Performance of MM/PBSA, MM/GBSA, and QM-MM/GBSA Approaches on Protein/Carbohydrate Complexes: Effect of Implicit Solvent Models, QM Methods, and Entropic Contributions. J Phys Chem B 2018; 122:8113-8121. [PMID: 30084252 DOI: 10.1021/acs.jpcb.8b03655] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Rapid and accurate binding affinity prediction of protein-carbohydrate complexes is a major challenge in glycomimetics design. Among the existing computational techniques, end-point methods have received considerable interest because of their low computational cost. However, significant obstacles remain when such methods are applied to protein-glycan complexes. This article reports the performance of end-point free-energy calculation methods: molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), MM/generalized Born surface area (MM/GBSA), and quantum mechanics-MM/GBSA (QM-MM/GBSA) on monosaccharides bound to RSL lectin from Ralstonia solanacearum. A careful investigation of the molecular dynamics simulation length, van der Waals radii sets, GB models, QM Hamiltonians, and entropic compensation has been made, and the results are compared with the experimental binding free energies from isothermal titration calorimetry/surface plasmon resonance measurements. The binding free energies using implicit solvent methods are found to be sensitive to the simulation length, radii set, GB model, and QM Hamiltonian. A simulation length of 10 ns using the radii set mbondi provides the best agreement with the experimental values ( r2 = 0.96) by MM/PBSA. The GBHCT model is in accord with the experimental values in MM/GBSA ( r2 = 0.91) or in combination with parameterized model number 6 (PM6) ( r2 = 0.98) in QM-MM/GBSA. Out of 12 QM Hamiltonians tested, PM6, density functional theory-based tight binding (DFTB), and their variants proved to be more efficient than other semiempirical methods. These methods perform equally well in predicting both absolute and relative binding free energies.
Collapse
Affiliation(s)
- Sushil K Mishra
- Central European Institute of Technology (CEITEC), and National Centre for Biomolecular Research, Faculty of Science , Masaryk University , Kamenice-5, 625 00 Brno , Czech Republic
| | - Jaroslav Koča
- Central European Institute of Technology (CEITEC), and National Centre for Biomolecular Research, Faculty of Science , Masaryk University , Kamenice-5, 625 00 Brno , Czech Republic
| |
Collapse
|
14
|
Gaieb Z, Liu S, Gathiaka S, Chiu M, Yang H, Shao C, Feher VA, Walters P, Kuhn B, Rudolph MG, Burley SK, Gilson MK, Amaro RE. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des 2018; 32:1-20. [PMID: 29204945 PMCID: PMC5767524 DOI: 10.1007/s10822-017-0088-4] [Citation(s) in RCA: 129] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/20/2017] [Indexed: 01/07/2023]
Abstract
The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank ( http://www.pdb.org ), and in affinity ranking and scoring of bound ligands.
Collapse
Affiliation(s)
- Zied Gaieb
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA 92093
| | - Shuai Liu
- Silicon Therapeutics, Boston MA 02210
| | | | - Michael Chiu
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA 92093
| | - Huanwang Yang
- RCSB Protein Data Bank Rutgers University, New Brunswick, NJ 08901
| | - Chenghua Shao
- RCSB Protein Data Bank Rutgers University, New Brunswick, NJ 08901
| | - Victoria A. Feher
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA 92093
| | | | - Bernd Kuhn
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Markus G. Rudolph
- Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | | | - Michael K. Gilson
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA 92093,Correspondence to:
| | - Rommie E. Amaro
- Drug Design Data Resource, University of California, San Diego, La Jolla, CA 92093,Correspondence to:
| |
Collapse
|
15
|
Mey ASJS, Jiménez JJ, Michel J. Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations. J Comput Aided Mol Des 2018; 32:199-210. [PMID: 29134431 PMCID: PMC5767197 DOI: 10.1007/s10822-017-0083-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 11/03/2017] [Indexed: 11/28/2022]
Abstract
The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.
Collapse
Affiliation(s)
- Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Jordi Juárez Jiménez
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK.
| |
Collapse
|
16
|
Georgiou C, McNae I, Wear M, Ioannidis H, Michel J, Walkinshaw M. Pushing the Limits of Detection of Weak Binding Using Fragment-Based Drug Discovery: Identification of New Cyclophilin Binders. J Mol Biol 2017; 429:2556-2570. [DOI: 10.1016/j.jmb.2017.06.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 06/15/2017] [Accepted: 06/21/2017] [Indexed: 12/12/2022]
|
17
|
Liu W, Jia X, Wang M, Li P, Wang X, Hu W, Zheng J, Mei Y. Calculations of the absolute binding free energies for Ralstonia solanacearum lectins bound with methyl-α-l-fucoside at molecular mechanical and quantum mechanical/molecular mechanical levels. RSC Adv 2017. [DOI: 10.1039/c7ra06215j] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
In this work, both a molecular mechanical (MM) method and a hybrid quantum mechanical/molecular mechanical (QM/MM) method have been applied in the study of the binding affinities of methyl-α-l-fucoside to Ralstonia solanacearum lectins.
Collapse
Affiliation(s)
- Wei Liu
- State Key Laboratory of Precision Spectroscopy
- School of Physics and Materials Science
- East China Normal University
- Shanghai 200062
- China
| | - Xiangyu Jia
- State Key Laboratory of Precision Spectroscopy
- School of Physics and Materials Science
- East China Normal University
- Shanghai 200062
- China
| | - Meiting Wang
- State Key Laboratory of Precision Spectroscopy
- School of Physics and Materials Science
- East China Normal University
- Shanghai 200062
- China
| | - Pengfei Li
- State Key Laboratory of Precision Spectroscopy
- School of Physics and Materials Science
- East China Normal University
- Shanghai 200062
- China
| | - Xiaohui Wang
- State Key Laboratory of Precision Spectroscopy
- School of Physics and Materials Science
- East China Normal University
- Shanghai 200062
- China
| | - Wenxin Hu
- The Computer Center
- School of Computer Science and Software Engineering
- East China Normal University
- Shanghai 200062
- China
| | - Jun Zheng
- The Computer Center
- School of Computer Science and Software Engineering
- East China Normal University
- Shanghai 200062
- China
| | - Ye Mei
- State Key Laboratory of Precision Spectroscopy
- School of Physics and Materials Science
- East China Normal University
- Shanghai 200062
- China
| |
Collapse
|
18
|
Investigating carbohydrate based ligands for galectin-3 with docking and molecular dynamics studies. J Mol Graph Model 2017; 71:211-217. [DOI: 10.1016/j.jmgm.2016.10.018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 10/22/2016] [Accepted: 10/29/2016] [Indexed: 11/21/2022]
|
19
|
Blinded predictions of binding modes and energies of HSP90-α ligands for the 2015 D3R grand challenge. Bioorg Med Chem 2016; 24:4890-4899. [DOI: 10.1016/j.bmc.2016.07.044] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/19/2016] [Accepted: 07/20/2016] [Indexed: 01/14/2023]
|
20
|
Bosisio S, Mey ASJS, Michel J. Blinded predictions of host-guest standard free energies of binding in the SAMPL5 challenge. J Comput Aided Mol Des 2016; 31:61-70. [PMID: 27503495 DOI: 10.1007/s10822-016-9933-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Accepted: 07/27/2016] [Indexed: 12/13/2022]
Abstract
In the context of the SAMPL5 blinded challenge standard free energies of binding were predicted for a dataset of 22 small guest molecules and three different host molecules octa-acids (OAH and OAMe) and a cucurbituril (CBC). Three sets of predictions were submitted, each based on different variations of classical molecular dynamics alchemical free energy calculation protocols based on the double annihilation method. The first model (model A) yields a free energy of binding based on computed free energy changes in solvated and host-guest complex phases; the second (model B) adds long range dispersion corrections to the previous result; the third (model C) uses an additional standard state correction term to account for the use of distance restraints during the molecular dynamics simulations. Model C performs the best in terms of mean unsigned error for all guests (MUE [Formula: see text]-95 % confidence interval) for the whole data set and in particular for the octa-acid systems (MUE [Formula: see text]). The overall correlation with experimental data for all models is encouraging ([Formula: see text]). The correlation between experimental and computational free energy of binding ranks as one of the highest with respect to other entries in the challenge. Nonetheless the large MUE for the best performing model highlights systematic errors, and submissions from other groups fared better with respect to this metric.
Collapse
Affiliation(s)
- Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh, EH9 3FJ, UK.
| |
Collapse
|
21
|
Calabrò G, Woods CJ, Powlesland F, Mey ASJS, Mulholland AJ, Michel J. Elucidation of Nonadditive Effects in Protein–Ligand Binding Energies: Thrombin as a Case Study. J Phys Chem B 2016; 120:5340-50. [DOI: 10.1021/acs.jpcb.6b03296] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Gaetano Calabrò
- EaStCHEM
School of Chemistry, University of Edinburgh, Joseph Black Building, King’s
Buildings, Edinburgh EH9
3JJ, U.K
| | | | - Francis Powlesland
- EaStCHEM
School of Chemistry, University of Edinburgh, Joseph Black Building, King’s
Buildings, Edinburgh EH9
3JJ, U.K
| | - Antonia S. J. S. Mey
- EaStCHEM
School of Chemistry, University of Edinburgh, Joseph Black Building, King’s
Buildings, Edinburgh EH9
3JJ, U.K
| | - Adrian J. Mulholland
- School
of Chemistry, Cantock’s close, University of Bristol, Bristol BS8 1TS, U.K
| | - Julien Michel
- EaStCHEM
School of Chemistry, University of Edinburgh, Joseph Black Building, King’s
Buildings, Edinburgh EH9
3JJ, U.K
| |
Collapse
|
22
|
Antonik PM, Volkov AN, Broder UN, Re DL, van Nuland NAJ, Crowley PB. Anomer-Specific Recognition and Dynamics in a Fucose-Binding Lectin. Biochemistry 2016; 55:1195-203. [PMID: 26845253 DOI: 10.1021/acs.biochem.5b01212] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Sugar binding by a cell surface ∼29 kDa lectin (RSL) from the bacterium Ralstonia solanacearum was characterized by NMR spectroscopy. The complexes formed with four monosaccharides and four fucosides were studied. Complete resonance assignments and backbone dynamics were determined for RSL in the sugar-free form and when bound to l-fucose or d-mannose. RSL was found to interact with both the α- and the β-anomer of l-fucose and the "fucose like" sugars d-arabinose and l-galactose. Peak splitting was observed for some resonances of the binding site residues. The assignment of the split signals to the α- or β-anomer was confirmed by comparison with the spectra of RSL bound to methyl-α-l-fucoside or methyl-β-l-fucoside. The backbone dynamics of RSL were sensitive to the presence of ligand, with the protein adopting a more compact structure upon binding to l-fucose. Taking advantage of tryptophan residues in the binding sites, we show that the indole resonance is an excellent reporter on ligand binding. Each sugar resulted in a distinct signature of chemical shift perturbations, suggesting that tryptophan signals are a sufficient probe of sugar binding.
Collapse
Affiliation(s)
- Paweł M Antonik
- School of Chemistry, National University of Ireland Galway , University Road, Galway, Ireland.,Department of Food BioSciences, Teagasc Food Research Centre , Ashtown, Dublin 15, Ireland
| | - Alexander N Volkov
- Jean Jeener NMR Centre, Structural Biology Brussels, Vrije Universiteit Brussel , Pleinlaan 2, 1050 Brussels, Belgium.,Structural Biology Research Centre, VIB, Pleinlaan 2, 1050 Brussels, Belgium
| | - Ursula N Broder
- School of Chemistry, National University of Ireland Galway , University Road, Galway, Ireland
| | - Daniele Lo Re
- School of Chemistry, National University of Ireland Galway , University Road, Galway, Ireland
| | - Nico A J van Nuland
- Jean Jeener NMR Centre, Structural Biology Brussels, Vrije Universiteit Brussel , Pleinlaan 2, 1050 Brussels, Belgium.,Structural Biology Research Centre, VIB, Pleinlaan 2, 1050 Brussels, Belgium
| | - Peter B Crowley
- School of Chemistry, National University of Ireland Galway , University Road, Galway, Ireland
| |
Collapse
|
23
|
Střelcová Z, Kulhánek P, Friák M, Fabritius HO, Petrov M, Neugebauer J, Koča J. The structure and dynamics of chitin nanofibrils in an aqueous environment revealed by molecular dynamics simulations. RSC Adv 2016. [DOI: 10.1039/c6ra00107f] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The structure–property relations reveal the typical size of chitin nanofibrils observed in natural systems.
Collapse
Affiliation(s)
- Zora Střelcová
- CEITEC – Central European Institute of Technology
- Masaryk University
- 625 00 Brno
- Czech Republic
- National Centre for Biomolecular Research
| | - Petr Kulhánek
- CEITEC – Central European Institute of Technology
- Masaryk University
- 625 00 Brno
- Czech Republic
- National Centre for Biomolecular Research
| | - Martin Friák
- CEITEC – Central European Institute of Technology
- Masaryk University
- 625 00 Brno
- Czech Republic
- Max-Planck-Institut für Eisenforschung GmbH
| | | | - Michal Petrov
- Max-Planck-Institut für Eisenforschung GmbH
- 40237 Düsseldorf
- Germany
| | - Jörg Neugebauer
- Max-Planck-Institut für Eisenforschung GmbH
- 40237 Düsseldorf
- Germany
| | - Jaroslav Koča
- CEITEC – Central European Institute of Technology
- Masaryk University
- 625 00 Brno
- Czech Republic
- National Centre for Biomolecular Research
| |
Collapse
|
24
|
Loeffler HH, Michel J, Woods C. FESetup: Automating Setup for Alchemical Free Energy Simulations. J Chem Inf Model 2015; 55:2485-90. [PMID: 26544598 DOI: 10.1021/acs.jcim.5b00368] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
FESetup is a new pipeline tool which can be used flexibly within larger workflows. The tool aims to support fast and easy setup of alchemical free energy simulations for molecular simulation packages such as AMBER, GROMACS, Sire, or NAMD. Post-processing methods like MM-PBSA and LIE can be set up as well. Ligands are automatically parametrized with AM1-BCC, and atom mappings for a single topology description are computed with a maximum common substructure search (MCSS) algorithm. An abstract molecular dynamics (MD) engine can be used for equilibration prior to free energy setup or standalone. Currently, all modern AMBER force fields are supported. Ease of use, robustness of the code, and automation where it is feasible are the main development goals. The project follows an open development model, and we welcome contributions.
Collapse
Affiliation(s)
- Hannes H Loeffler
- Scientific Computing Department, STFC Daresbury , Keckwick Lane, Warrington WA4 4AD, United Kingdom
| | - Julien Michel
- EaStCHEM School of Chemistry, University of Edinburgh , West Mains Road, Edinburgh EH9 3JJ, United Kingdom
| | - Christopher Woods
- BrisSynBio, University of Bristol , 8-10 Berkeley Square, Bristol BS8 1HH, United Kingdom
| |
Collapse
|