Accumulating evidence has suggested how mutational correlations between residues in a protein sequence alignments, observed in data spanning all branches of life, are induced by epistatic residue-residue interactions which can be traced to diverse features of a protein's structure, interactions and cellular environment. Methods to infer epistatic fitness landscapes based on these correlations have already demonstrated the links between fitness, sequence, and structure, but have raised new questions and challenges: How to account for phylogeny when analyzing covariation, and how does epistasis affect models of phylogeny? How does epistasis affect the course of evolution, for instance the evolutionary pathways leading to drug resistance? What level of model complexity is required to capture these epistatic fitness landscapes? I will discuss recent work related to development of these models and questions, using the kinase family as an example, and further discuss how these models predict detailed aspects of protein function which illustrate the connections between protein conformational and free energy landscapes and sequence landscapes.

Sequence covariation and evolutionary landscapes of proteins using Potts models

ACS Fall 2021

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