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

Yihang Wang, João Marcelo Lamim Ribeiro, Pratyush Tiwary

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

Abstract

The 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.

Original languageEnglish
Article number3573
JournalNature Communications
Volume10
Issue number1
DOIs
StatePublished - 1 Dec 2019
Externally publishedYes

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