Impact of phylogeny on the inference of functional sectors from protein sequence data

TL;DR

This study investigates how phylogenetic relationships influence the detection of functional sectors in proteins from sequence data, demonstrating that certain methods like ICOD are robust to phylogeny effects and combining different approaches enhances functional site identification.

Contribution

The paper introduces a controlled synthetic data approach to dissect phylogeny's impact on sector detection and evaluates the robustness of ICOD and conservation methods on real protein data.

Findings

ICOD is most robust to phylogenetic effects.

Conservation analysis is also quite robust.

Combining ICOD and conservation reveals complementary functional information.

Abstract

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.