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Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics

dc.contributor.authorTiwary, Pratyush
dc.date.accessioned2019-10-15T13:20:02Z
dc.date.available2019-10-15T13:20:02Z
dc.date.issued2019
dc.identifierhttps://doi.org/10.13016/masl-bs5g
dc.identifier.citationNATURE COMMUNICATIONS, (2019) 10:3573, https://doi.org/10.1038/s41467-019-11405-4 |en_US
dc.identifier.urihttp://hdl.handle.net/1903/25220
dc.descriptionPartial funding for Open Access provided by the UMD Libraries Open Access Publishing Fund.en_US
dc.description.abstractThe ability to rapidly learn from high-dimensional data to make reliable bets about the future is crucial in many contexts. This could be a fly avoiding predators, or the retina processing gigabytes of data to guide human actions. In this work we draw parallels between these and the efficient sampling of biomolecules with hundreds of thousands of atoms. For this we use the Predictive Information Bottleneck framework used for the first two problems, and re-formulate it for the sampling of biomolecules, especially when plagued with rare events. Our method uses a deep neural network to learn the minimally complex yet most predictive aspects of a given biomolecular trajectory. This information is used to perform iteratively biased simulations that enhance the sampling and directly obtain associated thermodynamic and kinetic information. We demonstrate the method on two test-pieces, studying processes slower than milliseconds, calculating free energies, kinetics and critical mutations.en_US
dc.language.isoen_USen_US
dc.publisherSpringerNatureen_US
dc.titlePast–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kineticsen_US
dc.typeArticleen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtChemistry & Biochemistryen_us
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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