How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?

By Matteo Figliuzzi, Pierre Barrat-Charlaix, Martin Weigt. 2018

117 citations recorded by the source corpus.

Abstract Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity to accurately predict residue‐residue contacts from sequence information alone, and thereby to facilitate tertiary and quaternary protein structure prediction. More recently, they have also been used to predict fitness effects of amino‐acid substitutions in proteins, and to predict evolutionary conserved protein‐protein interactions. These models are based on two currently unjustified hypotheses: 1) correlations in the amino‐acid usage of different positions are resulting collectively from networks of direct couplings; and 2) pairwise couplings are sufficient to capture the amino‐acid variability. Here, we propose a highly precise inference scheme based on Boltzmann‐machine learning, which allows us to systematically address these hypotheses. We show how correlations are built up in a highly collective way by a large number of coupling paths, which are based on the proteins three‐dimensional structure. We further find that pairwise coevolutionary models capture the collective residue variability across homologous proteins even for quantities which are not imposed by the inference procedure, like three‐residue correlations, the clustered structure of protein families in sequence space or the sequence distances between homologs. These findings strongly suggest that pairwise coevolutionary models are actually sufficient to accurately capture the residue variability in homologous protein families.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Generating functional protein variants with variational autoencoders (2020, methodology): “Recent advances in efficient inference 306 methods for these models have been shown to permit the generation of sequences which accurately 307 reproduce the low-order statistics from natural MSAs [44, 49].”
Direct coupling analysis of epistasis in allosteric materials (2018, unknown): “[41] on the distance between residues considered and see if the 3-body correlations are still captured when the residues are further apart.”
Efficient generative modeling of protein sequences using simple autoregressive models (2021, background): “However, plmDCA has limited capacities as a generative model [22]: symmetrization moves parameters away from their maximum-likelihood value, probably causing a loss in model accuracy.”