<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>9348</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Machine learning coarse-grained potentials of protein thermodynamics</title>
    <abstract language="eng">A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.</abstract>
    <parentTitle language="eng">Nature Communications</parentTitle>
    <identifier type="doi">10.1038/s41467-023-41343-1</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">29 August 2023</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Maciej Majewski</author>
    <submitter>Nicholas Charron</submitter>
    <author>Adrià Pérez</author>
    <author>Philipp Thölke</author>
    <author>Stefan Doerr</author>
    <author>Nicholas Charron</author>
    <author>Toni Giorgino</author>
    <author>Brooke Husic</author>
    <author>Cecilia Clementi</author>
    <author>Frank Noé</author>
    <author>Gianni De Fabritiis</author>
    <collection role="institutes" number="vas">Distributed Algorithms and Supercomputing</collection>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="persons" number="charron">Charron, Nicholas</collection>
  </doc>
</export-example>
