PT  - JOURNAL ARTICLE
AU  - Mohammad M. Sultan
AU  - Vijay S. Pande
TI  - Transferable tICA-Metadynamics: Efficient sampling of protein mutants by transferring information from the wild type’s Markov state model
AID  - 10.1101/158592
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 158592
4099  - http://biorxiv.org/content/early/2017/07/02/158592.short
4100  - http://biorxiv.org/content/early/2017/07/02/158592.full
AB  - 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 protein mutants by borrowing ideas from transfer learning methods in machine learning. Our method explicitly assumes that a similar set of slow modes and states are found in both the wild type 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 MBAR, 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 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.