Undersampling and the inference of coevolution in proteins

Cell Syst. 2023 Mar 15;14(3):210-219.e7. doi: 10.1016/j.cels.2022.12.013. Epub 2023 Jan 23.

Abstract

Protein structure, function, and evolution depend on local and collective epistatic interactions between amino acids. A powerful approach to defining these interactions is to construct models of couplings between amino acids that reproduce the empirical statistics (frequencies and correlations) observed in sequences comprising a protein family. The top couplings are then interpreted. Here, we show that as currently implemented, this inference unequally represents epistatic interactions, a problem that fundamentally arises from limited sampling of sequences in the context of distinct scales at which epistasis occurs in proteins. We show that these issues explain the ability of current approaches to predict tertiary contacts between amino acids and the inability to obviously expose larger networks of functionally relevant, collectively evolving residues called sectors. This work provides a necessary foundation for more deeply understanding and improving evolution-based models of proteins.

Keywords: DCA; Markov random field; Potts model; SCA; coevolution; contact prediction; generative model; protein; sector; undersampling.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Amino Acids*
  • Proteins* / metabolism

Substances

  • Proteins
  • Amino Acids