Quantum machine learning corrects classical forcefields: Stretching DNA base pairs in explicit solvent
| Autor: | Joshua T. Berryman, Amirhossein Taghavi, Florian Mazur, Alexandre Tkatchenko |
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| Přispěvatelé: | Fonds National de la Recherche - FnR [sponsor], University of Luxembourg: High Performance Computing - ULHPC [research center] |
| Rok vydání: | 2022 |
| Předmět: |
Chemical Physics (physics.chem-ph)
Quantitative Biology::Biomolecules Condensed Matter - Mesoscale and Nanoscale Physics Physics [G04] [Physical chemical mathematical & earth Sciences] FOS: Physical sciences General Physics and Astronomy DNA Molecular Dynamics Simulation simulation Machine Learning machine learning Physique [G04] [Physique chimie mathématiques & sciences de la terre] Biological Physics (physics.bio-ph) Physics - Chemical Physics Mesoscale and Nanoscale Physics (cond-mat.mes-hall) Solvents Physics - Biological Physics Physical and Theoretical Chemistry Base Pairing |
| Zdroj: | The Journal of chemical physics. 157(6) |
| ISSN: | 1089-7690 |
| Popis: | In order to improve the accuracy of molecular dynamics simulations, classical force fields are supplemented with a kernel-based machine learning method trained on quantum-mechanical fragment energies. As an example application, a potential-energy surface is generalised for a small DNA duplex, taking into account explicit solvation and long-range electron exchange--correlation effects. Study of the corrected potential energy versus extension shows that leading classical DNA models have excessive stiffness with respect to stretching. This discrepancy is found to be common across multiple forcefields. The quantum correction is in qualitative agreement to the experimental thermodynamics for larger DNA double helices, providing a candidate explanation for the general and long-standing discrepancy between single molecule stretching experiments and classical calculations of DNA stretching. The new dataset of quantum calculations and the associated Kernel Modified Molecular Dynamics (KMMD) method should be of general utility in biomolecular simulations. KMMD is made available as part of the AMBER22 simulation software. Comment: Publisher postprint (with minor corrections) is open access at J Chem Phys https://doi.org/10.1063/5.0094727 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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