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- W4301040365 abstract "We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced representations of protein dynamics that closely match physical reality. We derive an EM algorithm for learning, where the E-step extends the forward-backward algorithm for HMMs and the M-step requires the solution of large biconvex optimization problems. We construct an approximate semidefinite program solver based on the Frank-Wolfe algorithm and use it to solve the M-step. We apply our EM algorithm to learn accurate dynamics from large simulation datasets for the opioid peptide met-enkephalin and the proto-oncogene Src-kinase. Our learned models demonstrate significant improvements in temporal coherence over HMMs and standard switching models for met-enkephalin, and sample transition paths (possibly useful in rational drug design) for Src-kinase." @default.
- W4301040365 created "2022-10-04" @default.
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- W4301040365 date "2016-10-05" @default.
- W4301040365 modified "2023-10-16" @default.
- W4301040365 title "Learning Protein Dynamics with Metastable Switching Systems" @default.
- W4301040365 doi "https://doi.org/10.48550/arxiv.1610.01642" @default.
- W4301040365 hasPublicationYear "2016" @default.
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