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Woods CJ, Hedges LO, Mulholland AJ, Malaisree M, Tosco P, Loeffler HH, Suruzhon M, Burman M, Bariami S, Bosisio S, Calabro G, Clark F, Mey ASJS, Michel J. Sire: An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs. J Chem Phys 2024; 160:202503. [PMID: 38814008 DOI: 10.1063/5.0200458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company.
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Affiliation(s)
- Christopher J Woods
- Advanced Computing Research Centre, University of Bristol, Bristol, United Kingdom
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
| | - Lester O Hedges
- Advanced Computing Research Centre, University of Bristol, Bristol, United Kingdom
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
| | - Maturos Malaisree
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
| | | | | | | | - Matthew Burman
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
| | - Sofia Bariami
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stefano Bosisio
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Gaetano Calabro
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Finlay Clark
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Antonia S J S Mey
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
| | - Julien Michel
- OpenBioSim Community Interest Company, Edinburgh, United Kingdom
- EaStCHEM School of Chemistry, University of Edinburgh, Edinburgh, United Kingdom
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2
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Csizi K, Reiher M. Universal
QM
/
MM
approaches for general nanoscale applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | - Markus Reiher
- Laboratorium für Physikalische Chemie ETH Zürich Zürich Switzerland
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3
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Bieniek MK, Cree B, Pirie R, Horton JT, Tatum NJ, Cole DJ. An open-source molecular builder and free energy preparation workflow. Commun Chem 2022; 5:136. [PMID: 36320862 PMCID: PMC9607723 DOI: 10.1038/s42004-022-00754-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 10/11/2022] [Indexed: 01/27/2023] Open
Abstract
Automated free energy calculations for the prediction of binding free energies of congeneric series of ligands to a protein target are growing in popularity, but building reliable initial binding poses for the ligands is challenging. Here, we introduce the open-source FEgrow workflow for building user-defined congeneric series of ligands in protein binding pockets for input to free energy calculations. For a given ligand core and receptor structure, FEgrow enumerates and optimises the bioactive conformations of the grown functional group(s), making use of hybrid machine learning/molecular mechanics potential energy functions where possible. Low energy structures are optionally scored using the gnina convolutional neural network scoring function, and output for more rigorous protein-ligand binding free energy predictions. We illustrate use of the workflow by building and scoring binding poses for ten congeneric series of ligands bound to targets from a standard, high quality dataset of protein-ligand complexes. Furthermore, we build a set of 13 inhibitors of the SARS-CoV-2 main protease from the literature, and use free energy calculations to retrospectively compute their relative binding free energies. FEgrow is freely available at https://github.com/cole-group/FEgrow, along with a tutorial.
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Affiliation(s)
- Mateusz K. Bieniek
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Ben Cree
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Rachael Pirie
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Joshua T. Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
| | - Natalie J. Tatum
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH UK
| | - Daniel J. Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU UK
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4
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Alibay I, Magarkar A, Seeliger D, Biggin PC. Evaluating the use of absolute binding free energy in the fragment optimisation process. Commun Chem 2022; 5:105. [PMID: 36697714 PMCID: PMC9814858 DOI: 10.1038/s42004-022-00721-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/10/2022] [Indexed: 02/01/2023] Open
Abstract
Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.
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Affiliation(s)
- Irfan Alibay
- Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU, Oxford, UK
| | - Aniket Magarkar
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an de Riß, Germany
| | - Daniel Seeliger
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an de Riß, Germany
- Exscientia Inc, Office 400E, 2125 Biscayne Blvd, Miami, FL, 33137, USA
| | - Philip Charles Biggin
- Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU, Oxford, UK.
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5
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Mattei A, Hong RS, Dietrich H, Firaha D, Helfferich J, Liu YM, Sasikumar K, Abraham NS, Miglani Bhardwaj R, Neumann MA, Sheikh AY. Efficient Crystal Structure Prediction for Structurally Related Molecules with Accurate and Transferable Tailor-Made Force Fields. J Chem Theory Comput 2022; 18:5725-5738. [PMID: 35930763 PMCID: PMC9476662 DOI: 10.1021/acs.jctc.2c00451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Crystal structure prediction (CSP) his generally used to complement experimental solid form screening and applied to individual molecules in drug development. The fast development of algorithms and computing resources offers the opportunity to use CSP earlier and for a broader range of applications in the drug design cycle. This study presents a novel paradigm of CSP specifically designed for structurally related molecules, referred to as Quick-CSP. The approach prioritizes more accurate physics through robust and transferable tailor-made force fields (TMFFs), such that significant efficiency gains are achieved through the reduction of expensive ab initio calculations. The accuracy of the TMFF is increased by the introduction of electrostatic multipoles, and the fragment-based force field parameterization scheme is demonstrated to be transferable for a family of chemically related molecules. The protocol is benchmarked with structurally related compounds from the Bromodomain and Extraterminal (BET) domain inhibitors series. A new convergence criterion is introduced that aims at performing only as many ab initio optimizations of crystal structures as required to locate the bottom of the crystal energy landscape within a user-defined accuracy. The overall approach provides significant cost savings ranging from three- to eight-fold less than the full-CSP workflow. The reported advancements expand the scope and utility of the underlying CSP building blocks as well as their novel reassembly to other applications earlier in the drug design cycle to guide molecule design and selection.
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Affiliation(s)
- Alessandra Mattei
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Richard S Hong
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Hanno Dietrich
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Dzmitry Firaha
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Julian Helfferich
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Yifei Michelle Liu
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Kiran Sasikumar
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Nathan S Abraham
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Rajni Miglani Bhardwaj
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
| | - Marcus A Neumann
- Avant-garde Materials Simulation, GmbH, Alte Str. 2, 79249 Merzhausen, Germany
| | - Ahmad Y Sheikh
- Solid State Chemistry, Research & Development, AbbVie Inc., 1 N Waukegan Road, North Chicago, Illinois 60064, United States
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6
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Ringrose C, Horton JT, Wang LP, Cole DJ. Exploration and validation of force field design protocols through QM-to-MM mapping. Phys Chem Chem Phys 2022; 24:17014-17027. [PMID: 35792069 DOI: 10.1039/d2cp02864f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The scale of the parameter optimisation problem in traditional molecular mechanics force field construction means that design of a new force field is a long process, and sub-optimal choices made in the early stages can persist for many generations. We hypothesise that careful use of quantum mechanics to inform molecular mechanics parameter derivation (QM-to-MM mapping) should be used to significantly reduce the number of parameters that require fitting to experiment and increase the pace of force field development. Here, we design and train a collection of 15 new protocols for small, organic molecule force field derivation, and test their accuracy against experimental liquid properties. Our best performing model has only seven fitting parameters, yet achieves mean unsigned errors of just 0.031 g cm-3 and 0.69 kcal mol-1 in liquid densities and heats of vaporisation, compared to experiment. The software required to derive the designed force fields is freely available at https://github.com/qubekit/QUBEKit.
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Affiliation(s)
- Chris Ringrose
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
| | - Joshua T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
| | - Lee-Ping Wang
- Department of Chemistry, The University of California at Davis, Davis, California 95616, USA
| | - Daniel J Cole
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
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