Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.

ABSTRACT: During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here, we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra. They also allow us to efficiently simulate sequence libraries for a vast array of combinations of experimental parameters like sequence divergence, selection strength, and library size. We showcase the potential of the approach in reanalyzing two recent experiments to determine protein structure from signals of epistas

SUBMITTER: Bisardi M 

PROVIDER: S-EPMC8789065 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature