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Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning.


ABSTRACT: Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide time-series data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often biased by model parameters. Here, we devise a machine-learning method to combine the complementary information from the two approaches and construct a consistent model of conformational dynamics. It is applied to the folding dynamics of the formin-binding protein WW domain. MD simulations over 400 ?s led to an initial Markov state model (MSM), which was then "refined" using single-molecule Förster resonance energy transfer (FRET) data through hidden Markov modeling. The refined or data-assimilated MSM reproduces the FRET data and features hairpin one in the transition-state ensemble, consistent with mutation experiments. The folding pathway in the data-assimilated MSM suggests interplay between hydrophobic contacts and turn formation. Our method provides a general framework for investigating conformational transitions in other proteins.

SUBMITTER: Matsunaga Y 

PROVIDER: S-EPMC5933924 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Linking <i>time-series</i> of single-molecule experiments with molecular dynamics simulations by machine learning.

Matsunaga Yasuhiro Y   Sugita Yuji Y  

eLife 20180503


Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide <i>time-series</i> data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often biased by model parameters. Here, we devise a machine-learning method to combine the complementary information from the two approaches and construct a consistent model of conformational dynamics. I  ...[more]

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