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J Phys Chem B. 2018 May 31;122(21):5291-5299. doi: 10.1021/acs.jpcb.7b06896. Epub 2017 Oct 3.

Transfer Learning from Markov Models Leads to Efficient Sampling of Related Systems.

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1
Department of Chemistry , Stanford University , 318 Campus Drive , Stanford , California 94305 , United States.

Abstract

We recently showed that the time-structure-based independent component analysis method from Markov state model literature provided a set of variationally optimal slow collective variables for metadynamics (tICA-metadynamics). In this paper, we extend the methodology toward efficient sampling of related mutants by borrowing ideas from transfer learning methods in machine learning. Our method explicitly assumes that a similar set of slow modes and metastable states is found in both the wild type (baseline) and its mutants. Under this assumption, we describe a few simple techniques using sequence mapping for transferring the slow modes and structural information contained in the wild type simulation to a mutant model for performing enhanced sampling. The resulting simulations can then be reweighted onto the full-phase space using the multistate Bennett acceptance ratio, allowing for thermodynamic comparison against the wild type. We first benchmark our methodology by recapturing alanine dipeptide dynamics across a range of different atomistic force fields, including the polarizable Amoeba force field, after learning a set of slow modes using Amber ff99sb-ILDN. We next extend the method by including structural data from the wild type simulation and apply the technique to recapturing the effects of the GTT mutation on the FIP35 WW domain.

PMID:
28938073
DOI:
10.1021/acs.jpcb.7b06896

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