A variational autoencoder can guide adaptive molecular dynamics to fold Fs-peptide to 1.6 Å RMSD, and to approach the native state of a designed beta-beta-alpha protein at 4.4 Å.
Extensible and Scalable Adaptive Sampling on Supercomputers
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abstract
The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of High-Performance Computers (HPC) systems. Utilizing only "brute force" MD simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy the speed up can be more than one order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies, and reliably execute resulting workflows at scale on HPC platforms. Here the folding dynamics of four proteins are predicted with no a priori information.
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q-bio.BM 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Deep Generative Model Driven Protein Folding Simulation
A variational autoencoder can guide adaptive molecular dynamics to fold Fs-peptide to 1.6 Å RMSD, and to approach the native state of a designed beta-beta-alpha protein at 4.4 Å.