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AI3SD Video: Multiscale simulation of biomolecular mechanisms and dynamics: from enzyme evolution to receptor activation

AI3SD Video: Multiscale simulation of biomolecular mechanisms and dynamics: from enzyme evolution to receptor activation
AI3SD Video: Multiscale simulation of biomolecular mechanisms and dynamics: from enzyme evolution to receptor activation
Simulations are revealing detailed mechanisms of biomolecular systems and functionally relevant dynamics, and contributing to enzyme design. Biomolecular simulations can be used as computational ‘assays’ of biological activity, e.g. to predict drug resistance or the effects of mutation. Molecular simulation methods of various types are now capable of modelling processes ranging from biochemical reactions to membrane dynamics, and offer increasing predictive power. Recently, this has included identifying key features of SARS-CoV-2 proteins. Molecular dynamics (MD) simulations on long timescales can model substrate binding, and reveal dynamical changes associated with thermoadaptation and directed evolution of enzyme catalytic activity. MD simulations can calculate thermodynamic properties such as activation heat capacities. Increasingly, simulations are contributing to the design and engineering of natural enzymes and de novo biocatalysts. Interactive MD simulation in virtual reality allows direct manipulation of biological macromolecules, going beyond mere visualization to allow e.g. fully flexible docking of drugs into protein targets such as the SARS-CoV-2 main protease. Groups of researchers can work together in the same virtual environment. Mechanisms of signal transduction in receptors can be studied by a combination of equilibrium and nonequilibrium MD simulations, e.g. identifying a general mechanism of signal propagation in nicotinic acetylcholine receptors. Different types of application (e.g. ranging from chemical reactions to signal transduction) require different levels of treatment, which can be combined in multiscale models to tackle a range of time- and length-scales, e.g. to study drug metabolism by cytochrome P450 enzymes combining coarse-grained and atomistic MD and QM/MM methods. By coupling together different levels of description, multiscale methods can address e.g. how chemical changes in individual molecules cause changes at larger scales. QM/MM methods are an archetype of multiscale methods in biochemistry and can be used for modelling transition states and reaction intermediates, to identify catalytic interactions, and to analyse determinants of reactivity. QM/MM modelling can identify mechanisms of covalent inhibition and predict the activity of bacterial enzymes against antibiotics. References ‘Evolution of dynamical networks enhances catalysis in a designer enzyme’ H.A. Bunzel et al. Nature Chemistry, in press (2021). https://www.biorxiv.org/content/10.1101/2020.08.21.260885v1 ‘Designing better enzymes: Insights from directed evolution’ HA Bunzel, JLR Anderson, AJ Mulholland Current Opinion in Structural Biology 67, 212-218 (2021) ‘Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics’ I. Galdadas et al. eLife 10:e66567 DOI: 10.7554/eLife.66567 (2021) ‘Mechanism of covalent binding of ibrutinib to Bruton’s tyrosine kinase revealed by QM/MM calculations’ A Voice et al. Chemical Science https://doi.org/10.1039/D0SC06122K (2021) ‘Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease’ HM Deeks et al. Journal of Chemical Information and Modeling 60, 5803-5814 (2020) https://doi.org/10.1021/acs.jcim.0c01030 ‘Molecular Simulations suggest Vitamins, Retinoids and Steroids as Ligands of the Free Fatty Acid Pocket of the SARS-CoV-2 Spike Protein’ D.K. Shoemark et al. 133, 7174-7186 (2021) ‘Biomolecular Simulations in the Time of COVID-19, and After’ R.E. Amaro & A.J. Mulholland Computing in Science & Engineering 22, 30-36 (2020) DOI: 10.1109/MCSE.2020.3024155
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Mulholland, Adrian J.
31c4d9a5-7333-4829-8bbe-4bf69adf2aaa
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Mulholland, Adrian J.
31c4d9a5-7333-4829-8bbe-4bf69adf2aaa
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Mulholland, Adrian J. (2021) AI3SD Video: Multiscale simulation of biomolecular mechanisms and dynamics: from enzyme evolution to receptor activation. Kanza, Samantha, Frey, Jeremy G. and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0127).

Record type: Conference or Workshop Item (Other)

Abstract

Simulations are revealing detailed mechanisms of biomolecular systems and functionally relevant dynamics, and contributing to enzyme design. Biomolecular simulations can be used as computational ‘assays’ of biological activity, e.g. to predict drug resistance or the effects of mutation. Molecular simulation methods of various types are now capable of modelling processes ranging from biochemical reactions to membrane dynamics, and offer increasing predictive power. Recently, this has included identifying key features of SARS-CoV-2 proteins. Molecular dynamics (MD) simulations on long timescales can model substrate binding, and reveal dynamical changes associated with thermoadaptation and directed evolution of enzyme catalytic activity. MD simulations can calculate thermodynamic properties such as activation heat capacities. Increasingly, simulations are contributing to the design and engineering of natural enzymes and de novo biocatalysts. Interactive MD simulation in virtual reality allows direct manipulation of biological macromolecules, going beyond mere visualization to allow e.g. fully flexible docking of drugs into protein targets such as the SARS-CoV-2 main protease. Groups of researchers can work together in the same virtual environment. Mechanisms of signal transduction in receptors can be studied by a combination of equilibrium and nonequilibrium MD simulations, e.g. identifying a general mechanism of signal propagation in nicotinic acetylcholine receptors. Different types of application (e.g. ranging from chemical reactions to signal transduction) require different levels of treatment, which can be combined in multiscale models to tackle a range of time- and length-scales, e.g. to study drug metabolism by cytochrome P450 enzymes combining coarse-grained and atomistic MD and QM/MM methods. By coupling together different levels of description, multiscale methods can address e.g. how chemical changes in individual molecules cause changes at larger scales. QM/MM methods are an archetype of multiscale methods in biochemistry and can be used for modelling transition states and reaction intermediates, to identify catalytic interactions, and to analyse determinants of reactivity. QM/MM modelling can identify mechanisms of covalent inhibition and predict the activity of bacterial enzymes against antibiotics. References ‘Evolution of dynamical networks enhances catalysis in a designer enzyme’ H.A. Bunzel et al. Nature Chemistry, in press (2021). https://www.biorxiv.org/content/10.1101/2020.08.21.260885v1 ‘Designing better enzymes: Insights from directed evolution’ HA Bunzel, JLR Anderson, AJ Mulholland Current Opinion in Structural Biology 67, 212-218 (2021) ‘Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics’ I. Galdadas et al. eLife 10:e66567 DOI: 10.7554/eLife.66567 (2021) ‘Mechanism of covalent binding of ibrutinib to Bruton’s tyrosine kinase revealed by QM/MM calculations’ A Voice et al. Chemical Science https://doi.org/10.1039/D0SC06122K (2021) ‘Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease’ HM Deeks et al. Journal of Chemical Information and Modeling 60, 5803-5814 (2020) https://doi.org/10.1021/acs.jcim.0c01030 ‘Molecular Simulations suggest Vitamins, Retinoids and Steroids as Ligands of the Free Fatty Acid Pocket of the SARS-CoV-2 Spike Protein’ D.K. Shoemark et al. 133, 7174-7186 (2021) ‘Biomolecular Simulations in the Time of COVID-19, and After’ R.E. Amaro & A.J. Mulholland Computing in Science & Engineering 22, 30-36 (2020) DOI: 10.1109/MCSE.2020.3024155

Video
AI4Proteins-Seminar-Series-AdrianMulholland-260521 - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 26 May 2021
Additional Information: Adrian Mulholland is a Professor of Chemistry and School Research Director in the School of Chemistry at the University of Bristol. His research focuses on the investigation of mechanisms of enzyme catalysis, and biomolecular structure and function more generally, by computational modelling and simulation. He was awarded the 2020 John Meurig Thomas Medal ‘for outstanding and innovative work in catalytic science’ and is an ERC Advanced Grant holder.
Venue - Dates: AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords: AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins

Identifiers

Local EPrints ID: 450596
URI: http://eprints.soton.ac.uk/id/eprint/450596
PURE UUID: 581d3708-24ef-4d9f-b2c6-b7b3f3807fcf
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 04 Aug 2021 16:35
Last modified: 05 Aug 2021 01:53

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Contributors

Author: Adrian J. Mulholland
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan

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