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

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.