Ab initio; Biomolecular dynamics; Chemical fragments; Complex Processes; Dynamics simulation; Forcefields; Large system; Mechanical force fields; Quantum mechanical force; Quantum-mechanical calculation; Multidisciplinary
Résumé :
[en] Molecular dynamics (MD) simulations allow insights into complex processes, but accurate MD simulations require costly quantum-mechanical calculations. For larger systems, efficient but less reliable empirical force fields are used. Machine-learned force fields (MLFFs) offer similar accuracy as ab initio methods at orders-of-magnitude speedup, but struggle to model long-range interactions in large molecules. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on “bottom-up” and “top-down” molecular fragments, from which the relevant interactions can be learned. GEMS allows nanosecond-scale MD simulations of >25,000 atoms at essentially ab initio quality, correctly predicts dynamical oscillations between different helical motifs in polyalanine, and yields good agreement with terahertz vibrational spectroscopy for large-scale protein-water fluctuations in solvated crambin. Our analyses indicate that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes.
Centre de recherche :
ULHPC - University of Luxembourg: High Performance Computing
Unke, Oliver T ; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland ; Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany ; DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany
Stöhr, Martin ; Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
Ganscha, Stefan ; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
Unterthiner, Thomas ; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
Maennel, Hartmut ; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
Kashubin, Sergii ; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
Ahlin, Daniel ; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland
Gastegger, Michael ; Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany ; DFG Cluster of Excellence "Unifying Systems in Catalysis" (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany ; BASLEARN - TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany
Medrano Sandonas, Leonardo ; Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
TKATCHENKO, Alexandre ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)
Müller, Klaus-Robert ✱; Google DeepMind, Tucholskystraße 2, 10117 Berlin, Germany and Brandschenkestrasse 110, 8002 Zürich, Switzerland ; Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany ; Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea ; Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany ; BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
✱ Ces auteurs ont contribué de façon équivalente à la publication.
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.
Date de publication/diffusion :
05 avril 2024
Titre du périodique :
Science Advances
eISSN :
2375-2548
Maison d'édition :
American Association for the Advancement of Science, Etats-Unis
Volume/Tome :
10
Fascicule/Saison :
14
Pagination :
eadn4397
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Physics and Materials Science Computational Sciences
SNSF - Swiss National Science Foundation FNR - Fonds National de la Recherche BMBF - Bundesministerium für Bildung und Forschung IITP - Institute for Information and communications Technology Promotion ERC - European Research Council Union Européenne
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