Transition paths in Potts-like energy landscapes: General properties and application to protein sequence models.

We study transition paths in energy landscapes over multicategorical Potts configurations using the mean-field approach introduced by Mauri et al. [Phys. Rev.

Mutational Paths with Sequence-Based Models of Proteins: From Sampling to Mean-Field Characterization.

Identifying and characterizing mutational paths is an important issue in evolutionary biology, with potential applications to bioengineering. We here propose an algorithm to sample mutational paths, which we benchmark on exactly solvable models of proteins in silico, and apply to data-driven models of natural proteins learned from sequence data with restricted Boltzmann machines. We then use mean-field theory to characterize paths for different mutational dynamics of interest, and to extend Kimura's estimate of evolutionary distances to sequence-based epistatic models of selection.