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Young TA, Johnston-Wood T, Zhang H, Duarte F. Reaction dynamics of Diels-Alder reactions from machine learned potentials. Phys Chem Chem Phys 2022; 24:20820-20827. [PMID: 36004770 DOI: 10.1039/d2cp02978b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Recent advances in the development of reactive machine-learned potentials (MLPs) promise to transform reaction modelling. However, such methods have remained computationally expensive and limited to experts. Here, we employ different MLP methods (ACE, NequIP, GAP), combined with automated fitting and active learning, to study the reaction dynamics of representative Diels-Alder reactions. We demonstrate that the ACE and NequIP MLPs can consistently achieve chemical accuracy (±1 kcal mol-1) to the ground-truth surface with only a few hundred reference calculations. These strategies are shown to enable routine ab initio-quality classical and quantum dynamics, and obtain dynamical quantities such as product ratios and free energies from non-static methods. For ambimodal reactions, product distributions were found to be strongly dependent on the QM method and less so on the type of dynamics propagated.
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Affiliation(s)
- Tom A Young
- Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA, UK.
| | | | - Hanwen Zhang
- Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA, UK.
| | - Fernanda Duarte
- Chemistry Research Laboratory, 12 Mansfield Road, Oxford, OX1 3TA, UK.
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Ambrose B, Hill VE, Shaw RA, Johnston-Wood T, Willmott M, Johnston C, Mächtel R, Cordes T, Lerner E, Hill J, Weiss S, Craggs TD. A novel single molecule fluorescence quenching technique for measuring distances below 3 nm. Biophys J 2022. [DOI: 10.1016/j.bpj.2021.11.613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Chan HTH, Moesser MA, Walters RK, Malla TR, Twidale RM, John T, Deeks HM, Johnston-Wood T, Mikhailov V, Sessions RB, Dawson W, Salah E, Lukacik P, Strain-Damerell C, Owen CD, Nakajima T, Świderek K, Lodola A, Moliner V, Glowacki DR, Spencer J, Walsh MA, Schofield CJ, Genovese L, Shoemark DK, Mulholland AJ, Duarte F, Morris GM. Discovery of SARS-CoV-2 M pro peptide inhibitors from modelling substrate and ligand binding. Chem Sci 2021; 12:13686-13703. [PMID: 34760153 PMCID: PMC8549791 DOI: 10.1039/d1sc03628a] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 09/05/2021] [Indexed: 12/22/2022] Open
Abstract
The main protease (Mpro) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural Mpro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular plasticity at the S2 site. Building on our initial Mpro-substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective 'peptibitors' inhibit Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.
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Affiliation(s)
- H T Henry Chan
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Marc A Moesser
- Department of Statistics, University of Oxford 24-29 St Giles' Oxford OX1 3LB UK
| | - Rebecca K Walters
- Centre for Computational Chemistry, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
- Intangible Realities Laboratory, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Tika R Malla
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Rebecca M Twidale
- Centre for Computational Chemistry, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Tobias John
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Helen M Deeks
- Centre for Computational Chemistry, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
- Intangible Realities Laboratory, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Tristan Johnston-Wood
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Victor Mikhailov
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Richard B Sessions
- School of Biochemistry, University of Bristol, Medical Sciences Building University Walk Bristol BS8 1TD UK
| | - William Dawson
- RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Eidarus Salah
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Petra Lukacik
- Diamond Light Source Ltd, Harwell Science and Innovation Campus Didcot OX11 0DE UK
- Research Complex at Harwell, Harwell Science and Innovation Campus Didcot OX11 0FA UK
| | - Claire Strain-Damerell
- Diamond Light Source Ltd, Harwell Science and Innovation Campus Didcot OX11 0DE UK
- Research Complex at Harwell, Harwell Science and Innovation Campus Didcot OX11 0FA UK
| | - C David Owen
- Diamond Light Source Ltd, Harwell Science and Innovation Campus Didcot OX11 0DE UK
- Research Complex at Harwell, Harwell Science and Innovation Campus Didcot OX11 0FA UK
| | - Takahito Nakajima
- RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe Hyogo 650-0047 Japan
| | - Katarzyna Świderek
- Biocomp Group, Institute of Advanced Materials (INAM), Universitat Jaume I 12071 Castello Spain
| | - Alessio Lodola
- Food and Drug Department, University of Parma Parco Area delle Scienze, 27/A 43124 Parma Italy
| | - Vicent Moliner
- Biocomp Group, Institute of Advanced Materials (INAM), Universitat Jaume I 12071 Castello Spain
| | - David R Glowacki
- Intangible Realities Laboratory, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - James Spencer
- Intangible Realities Laboratory, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Martin A Walsh
- Diamond Light Source Ltd, Harwell Science and Innovation Campus Didcot OX11 0DE UK
- Research Complex at Harwell, Harwell Science and Innovation Campus Didcot OX11 0FA UK
| | - Christopher J Schofield
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Luigi Genovese
- Univ. Grenoble Alpes, CEA, IRIG-MEM-L_Sim 38000 Grenoble France
| | - Deborah K Shoemark
- School of Biochemistry, University of Bristol, Medical Sciences Building University Walk Bristol BS8 1TD UK
| | - Adrian J Mulholland
- Centre for Computational Chemistry, School of Chemistry, University of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Fernanda Duarte
- Chemistry Research Laboratory, Department of Chemistry and the Ineos Oxford Institute for Antimicrobial Research 12 Mansfield Road Oxford OX1 3TA UK
| | - Garrett M Morris
- Department of Statistics, University of Oxford 24-29 St Giles' Oxford OX1 3LB UK
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Young TA, Johnston-Wood T, Deringer VL, Duarte F. A transferable active-learning strategy for reactive molecular force fields. Chem Sci 2021; 12:10944-10955. [PMID: 34476072 PMCID: PMC8372546 DOI: 10.1039/d1sc01825f] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/04/2021] [Indexed: 11/25/2022] Open
Abstract
Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but doing so typically requires considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models can be developed for diverse chemical systems in an autonomous manner, requiring only hundreds to a few thousand energy and gradient evaluations on a reference potential-energy surface. The approach uses separate intra- and inter-molecular fits and employs a prospective error metric to assess the accuracy of the potentials. We demonstrate applications to a range of molecular systems with relevance to computational organic chemistry: ranging from bulk solvents, a solvated metal ion and a metallocage onwards to chemical reactivity, including a bifurcating Diels-Alder reaction in the gas phase and non-equilibrium dynamics (a model SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for reactive molecular systems.
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Affiliation(s)
- Tom A Young
- Chemistry Research Laboratory, University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Tristan Johnston-Wood
- Chemistry Research Laboratory, University of Oxford Mansfield Road Oxford OX1 3TA UK
| | - Volker L Deringer
- Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford Oxford OX1 3QR UK
| | - Fernanda Duarte
- Chemistry Research Laboratory, University of Oxford Mansfield Road Oxford OX1 3TA UK
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Shaw RA, Johnston-Wood T, Ambrose B, Craggs TD, Hill JG. CHARMM-DYES: Parameterization of Fluorescent Dyes for Use with the CHARMM Force Field. J Chem Theory Comput 2020; 16:7817-7824. [DOI: 10.1021/acs.jctc.0c00721] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Robert A. Shaw
- Department of Chemistry, University of Sheffield, Sheffield S3 7HF, U.K
- Present address: ARC Centre of Excellence in Exciton Science, School of Science, RMIT University, Melbourne, VIC 3000, Australia
| | - Tristan Johnston-Wood
- Department of Chemistry, University of Sheffield, Sheffield S3 7HF, U.K
- Present address: Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, Oxford OX1 3QZ, U.K
| | - Benjamin Ambrose
- Department of Chemistry, University of Sheffield, Sheffield S3 7HF, U.K
| | - Timothy D. Craggs
- Department of Chemistry, University of Sheffield, Sheffield S3 7HF, U.K
| | - J. Grant Hill
- Department of Chemistry, University of Sheffield, Sheffield S3 7HF, U.K
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Ambrose B, Willmott M, Johnston-Wood T, Shaw RA, Hill J, Craggs TD. Below the FRET Limit: A New Quantitative Single-Molecule Tool for Measuring Short-Range (0-3 NM) Biomolecular Conformations. Biophys J 2020. [DOI: 10.1016/j.bpj.2019.11.3319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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