Revealing evolutionary constraints on proteins through sequence analysis

TL;DR

This paper demonstrates that evolutionary constraints on proteins, reflected as coevolving amino acid sectors, can be explained by selection on functional properties, and introduces a method to identify these sectors from sequence data.

Contribution

It shows that selection on functional traits causes coevolutionary sectors in proteins and proposes a new method to detect these sectors from sequence covariance data.

Findings

Selection on functional properties leads to coevolving amino acid sectors.

Small-eigenvalue modes of covariance matrices reveal functional sectors.

The proposed method robustly identifies sectors and mutational effects.

Abstract

Statistical analysis of alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.