Protein sectors: statistical coupling analysis versus conservation

TL;DR

This paper critically examines statistical coupling analysis (SCA) in proteins, revealing that for proteins with a single sector, conservation dominates SCA results, suggesting a need to study multi-sector proteins for more functional insights.

Contribution

It demonstrates that in single-sector proteins, SCA results are primarily driven by conservation, challenging previous assumptions about coevolution signals.

Findings

SCA results in single-sector proteins are mainly due to conservation.

Conservation alone can predict functional sites in these proteins.

Studying proteins with multiple sectors could reveal more about coevolution.

Abstract

Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed "sectors". The method applies spectral analysis to a matrix obtained by combining correlation information with sequence conservation. It has been asserted that the protein sectors identified by SCA are functionally significant, with different sectors controlling different biochemical properties of the protein. Here we reconsider the available experimental data and note that it involves almost exclusively proteins with a single sector. We show that in this case sequence conservation is the dominating factor in SCA, and can alone be used to make statistically equivalent functional predictions. Therefore, we suggest shifting the experimental focus to proteins for which SCA identifies several sectors. Correlations in protein alignments, which have been shown to be informative in a number of independent studies, would then be less dominated by sequence conservation.