Machine-Learning a Transferable Coarse-grained Protein Force Field

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• Felix Musil, Free University Berlin, Germany
• Monday 27 June 2022, 14:30-15:00
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If you have a question about this talk, please contact Dr Venkat Kapil.

Venue to be confirmed

Recent developments and applications of machine learning to physical systems have led to significant advances in the construction of coarse-grained force fields for efficient simulation and sampling [1]. Yet, transferability and extrapolation between different systems of interest remain an outstanding limitation for machine-learned models. Using force matching, a bottom-up coarse-graining approach, and a database of chemically diverse peptides, we present a coarse-grained force field that is transferable across protein sequences enabling us to explore their conformational landscape. Our model, based on a graph neural network architecture, is validated/tested against all-atom simulations of unseen proteins [2].

[1] Wang, J. et al. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Cent. Sci. 5, 755–767 (2019).

[2] Lindorff-Larsen, K. et al. How fast-folding proteins fold. Science (80-. ). 334, 517–520 (2011).

This talk is part of the Lennard-Jones Centre series.

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• Hanchen DaDaDash
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