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Transfer Learning from Markov models leads to efficient sampling of related systems

Mohammad M. Sultan, Vijay S. Pande
doi: https://doi.org/10.1101/158592
Mohammad M. Sultan
1Department of Chemistry, Stanford University, 318 Campus Drive, Stanford, California 94305, USA
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Vijay S. Pande
1Department of Chemistry, Stanford University, 318 Campus Drive, Stanford, California 94305, USA
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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 towards 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 are found in both the wild type (base line) 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 Multi-state Bennett Acceptance Ratio, allowing for thermodynamic comparison against the wild type. We first benchmark our methodology by re-capturing 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 affects of the GTT mutation on the FIP35 WW domain.

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Posted September 21, 2017.
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Transfer Learning from Markov models leads to efficient sampling of related systems
Mohammad M. Sultan, Vijay S. Pande
bioRxiv 158592; doi: https://doi.org/10.1101/158592
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Transfer Learning from Markov models leads to efficient sampling of related systems
Mohammad M. Sultan, Vijay S. Pande
bioRxiv 158592; doi: https://doi.org/10.1101/158592

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