Scientific ReportsBioinformaticsPeer-reviewed journal article
Extracting the phylogenetic dimension of coevolution reveals hidden functional signal
Alexandre ColaviniD, Esha Atolia, Anne‐Florence BitboliD, Kerwyn Casey HuangiD
Abstract
Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a proteins phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a method for explicitly defining a phylogenetic dimension of coevolution signal, and demonstrate that coevolution can occur on multiple phylogenetic timescales within a single protein. Our method, Nested Coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved structural-contact prediction over gold-standard coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after NC correction were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Our conceptualization of the phylogenetic separation of coevolution represents an advance from previous pragmatic attempts to reduce phylogenetic artifacts in measurements of coevolution. Application of NC broadens the application of protein coevolution measurements, particularly to eukaryotic proteins with fewer naturally available sequences, and further elucidates relationships among protein evolution and genetic diseases.
- assay: protein sequence
- research focus: protein structure prediction
- research focus: protein design
- research focus: benchmarking
- approach: statistical model
- Citations
- 19
- FWCI
- 1.22
- Field percentile
- top 23%
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