Impact of phylogeny on the inference of functional sectors from protein sequence data
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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 natural 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.
Author Summary
Proteins perform crucial functions in the cell. The biological function of a protein is encoded in its amino-acid sequence. Natural selection acts at the level of function, while mutations arise randomly on sequences. In alignments of sequences of homologous proteins, which share common ancestry and common function, the amino acid usages at different sites can be correlated due to functional constraints. In particular, groups of collectively correlated amino acids, termed sectors, tend to emerge due to selection on functional traits. However, correlations can also arise from the shared evolutionary history of homologous proteins, even without functional constraints. This may obscure the inference of functional sectors. By analyzing controlled synthetic data as well as natural protein sequence data, we show that two very different methods allow to identify sectors and mutational effects in a way that is most robust to phylogeny. We suggest that considering both of these methods allows a better identification of functionally important sites from protein sequences. These results have potential impact on the design of new functional sequences.
