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Correlated Evolution of Nearby Residues in Drosophilid Proteins
Author(s) -
Benjamin J. Callahan,
Richard A. Neher,
Doris Bachtrog,
Peter Andolfatto,
Boris I. Shraiman
Publication year - 2011
Publication title -
plos genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.587
H-Index - 233
eISSN - 1553-7404
pISSN - 1553-7390
DOI - 10.1371/journal.pgen.1001315
Subject(s) - biology , genetics , negative selection , lineage (genetic) , epistasis , population , drosophila melanogaster , evolutionary biology , melanogaster , protein sequencing , protein structure , genome , peptide sequence , gene , biochemistry , demography , sociology
Here we investigate the correlations between coding sequence substitutions as a function of their separation along the protein sequence. We consider both substitutions between the reference genomes of several Drosophilids as well as polymorphisms in a population sample of Zimbabwean Drosophila melanogaster. We find that amino acid substitutions are "clustered" along the protein sequence, that is, the frequency of additional substitutions is strongly enhanced within ≈10 residues of a first such substitution. No such clustering is observed for synonymous substitutions, supporting a "correlation length" associated with selection on proteins as the causative mechanism. Clustering is stronger between substitutions that arose in the same lineage than it is between substitutions that arose in different lineages. We consider several possible origins of clustering, concluding that epistasis (interactions between amino acids within a protein that affect function) and positional heterogeneity in the strength of purifying selection are primarily responsible. The role of epistasis is directly supported by the tendency of nearby substitutions that arose on the same lineage to preserve the total charge of the residues within the correlation length and by the preferential cosegregation of neighboring derived alleles in our population sample. We interpret the observed length scale of clustering as a statistical reflection of the functional locality (or modularity) of proteins: amino acids that are near each other on the protein backbone are more likely to contribute to, and collaborate toward, a common subfunction

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