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High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling
We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted w...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179654/ https://www.ncbi.nlm.nih.gov/pubmed/34168762 http://dx.doi.org/10.1039/d1sc00145k |
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author | Jaffrelot Inizan, Théo Célerse, Frédéric Adjoua, Olivier El Ahdab, Dina Jolly, Luc-Henri Liu, Chengwen Ren, Pengyu Montes, Matthieu Lagarde, Nathalie Lagardère, Louis Monmarché, Pierre Piquemal, Jean-Philip |
author_facet | Jaffrelot Inizan, Théo Célerse, Frédéric Adjoua, Olivier El Ahdab, Dina Jolly, Luc-Henri Liu, Chengwen Ren, Pengyu Montes, Matthieu Lagarde, Nathalie Lagardère, Louis Monmarché, Pierre Piquemal, Jean-Philip |
author_sort | Jaffrelot Inizan, Théo |
collection | PubMed |
description | We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted within a selective process to achieve sufficient phase-space sampling. Accurate statistical properties can be obtained through reweighting. Within this highly parallel setup, the Tinker-HP package can be powered by an arbitrary large number of GPUs on supercomputers, reducing exploration time from years to days. This approach is used to tackle the urgent modeling problem of the SARS-CoV-2 Main Protease (M(pro)) producing more than 38 μs of all-atom simulations of its apo (ligand-free) dimer using the high-resolution AMOEBA PFF. The first 15.14 μs simulation (physiological pH) is compared to available non-PFF long-timescale simulation data. A detailed clustering analysis exhibits striking differences between FFs, with AMOEBA showing a richer conformational space. Focusing on key structural markers related to the oxyanion hole stability, we observe an asymmetry between protomers. One of them appears less structured resembling the experimentally inactive monomer for which a 6 μs simulation was performed as a basis for comparison. Results highlight the plasticity of the M(pro) active site. The C-terminal end of its less structured protomer is shown to oscillate between several states, being able to interact with the other protomer, potentially modulating its activity. Active and distal site volumes are found to be larger in the most active protomer within our AMOEBA simulations compared to non-PFFs as additional cryptic pockets are uncovered. A second 17 μs AMOEBA simulation is performed with protonated His172 residues mimicking lower pH. Data show the protonation impact on the destructuring of the oxyanion loop. We finally analyze the solvation patterns around key histidine residues. The confined AMOEBA polarizable water molecules are able to explore a wide range of dipole moments, going beyond bulk values, leading to a water molecule count consistent with experimental data. Results suggest that the use of PFFs could be critical in drug discovery to accurately model the complexity of the molecular interactions structuring M(pro). |
format | Online Article Text |
id | pubmed-8179654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-81796542021-06-23 High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling Jaffrelot Inizan, Théo Célerse, Frédéric Adjoua, Olivier El Ahdab, Dina Jolly, Luc-Henri Liu, Chengwen Ren, Pengyu Montes, Matthieu Lagarde, Nathalie Lagardère, Louis Monmarché, Pierre Piquemal, Jean-Philip Chem Sci Chemistry We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted within a selective process to achieve sufficient phase-space sampling. Accurate statistical properties can be obtained through reweighting. Within this highly parallel setup, the Tinker-HP package can be powered by an arbitrary large number of GPUs on supercomputers, reducing exploration time from years to days. This approach is used to tackle the urgent modeling problem of the SARS-CoV-2 Main Protease (M(pro)) producing more than 38 μs of all-atom simulations of its apo (ligand-free) dimer using the high-resolution AMOEBA PFF. The first 15.14 μs simulation (physiological pH) is compared to available non-PFF long-timescale simulation data. A detailed clustering analysis exhibits striking differences between FFs, with AMOEBA showing a richer conformational space. Focusing on key structural markers related to the oxyanion hole stability, we observe an asymmetry between protomers. One of them appears less structured resembling the experimentally inactive monomer for which a 6 μs simulation was performed as a basis for comparison. Results highlight the plasticity of the M(pro) active site. The C-terminal end of its less structured protomer is shown to oscillate between several states, being able to interact with the other protomer, potentially modulating its activity. Active and distal site volumes are found to be larger in the most active protomer within our AMOEBA simulations compared to non-PFFs as additional cryptic pockets are uncovered. A second 17 μs AMOEBA simulation is performed with protonated His172 residues mimicking lower pH. Data show the protonation impact on the destructuring of the oxyanion loop. We finally analyze the solvation patterns around key histidine residues. The confined AMOEBA polarizable water molecules are able to explore a wide range of dipole moments, going beyond bulk values, leading to a water molecule count consistent with experimental data. Results suggest that the use of PFFs could be critical in drug discovery to accurately model the complexity of the molecular interactions structuring M(pro). The Royal Society of Chemistry 2021-02-02 /pmc/articles/PMC8179654/ /pubmed/34168762 http://dx.doi.org/10.1039/d1sc00145k Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Jaffrelot Inizan, Théo Célerse, Frédéric Adjoua, Olivier El Ahdab, Dina Jolly, Luc-Henri Liu, Chengwen Ren, Pengyu Montes, Matthieu Lagarde, Nathalie Lagardère, Louis Monmarché, Pierre Piquemal, Jean-Philip High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
title | High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
title_full | High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
title_fullStr | High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
title_full_unstemmed | High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
title_short | High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
title_sort | high-resolution mining of the sars-cov-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179654/ https://www.ncbi.nlm.nih.gov/pubmed/34168762 http://dx.doi.org/10.1039/d1sc00145k |
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