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Multiscale Enhanced Sampling Using Machine Learning

Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace gene...

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Autor principal: Moritsugu, Kei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540671/
https://www.ncbi.nlm.nih.gov/pubmed/34685447
http://dx.doi.org/10.3390/life11101076
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author Moritsugu, Kei
author_facet Moritsugu, Kei
author_sort Moritsugu, Kei
collection PubMed
description Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble.
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spelling pubmed-85406712021-10-24 Multiscale Enhanced Sampling Using Machine Learning Moritsugu, Kei Life (Basel) Article Multiscale enhanced sampling (MSES) allows for an enhanced sampling of all-atom protein structures by coupling with the accelerated dynamics of the associated coarse-grained (CG) model. In this paper, we propose an MSES extension to replace the CG model with the dynamics on the reduced subspace generated by a machine learning approach, the variational autoencoder (VAE). The molecular dynamic (MD) trajectories of the ribose-binding protein (RBP) in both the closed and open forms were used as the input by extracting the inter-residue distances as the structural features in order to train the VAE model, allowing the encoded latent layer to characterize the difference in the structural dynamics of the closed and open forms. The interpolated data characterizing the RBP structural change in between the closed and open forms were thus efficiently generated in the low-dimensional latent space of the VAE, which was then decoded into the time-series data of the inter-residue distances and was useful for driving the structural sampling at an atomistic resolution via the MSES scheme. The free energy surfaces on the latent space demonstrated the refinement of the generated data that had a single basin into the simulated data containing two closed and open basins, thus illustrating the usefulness of the MD simulation together with the molecular mechanics force field in recovering the correct structural ensemble. MDPI 2021-10-12 /pmc/articles/PMC8540671/ /pubmed/34685447 http://dx.doi.org/10.3390/life11101076 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Moritsugu, Kei
Multiscale Enhanced Sampling Using Machine Learning
title Multiscale Enhanced Sampling Using Machine Learning
title_full Multiscale Enhanced Sampling Using Machine Learning
title_fullStr Multiscale Enhanced Sampling Using Machine Learning
title_full_unstemmed Multiscale Enhanced Sampling Using Machine Learning
title_short Multiscale Enhanced Sampling Using Machine Learning
title_sort multiscale enhanced sampling using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540671/
https://www.ncbi.nlm.nih.gov/pubmed/34685447
http://dx.doi.org/10.3390/life11101076
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