Computational protein design quantifies structural constraints on amino acid covariation.
Amino acid covariation, where the identities of amino acids at different sequence positions are correlated, is a hallmark of naturally occurring proteins. This covariation can arise from multiple factors, including selective pressures for maintaining protein structure, requirements imposed by a specific function, or from phylogenetic sampling bias. Here we employed flexible backbone computational protein design to quantify the extent to which protein structure has constrained amino acid covariation for 40 diverse protein domains. We find significant similarities between the amino acid covariation in alignments of natural protein sequences and sequences optimized for their structures by computational protein design methods. These results indicate that the structural constraints imposed by protein architecture play a dominant role in shaping amino acid covariation and that computational protein design methods can capture these effects. We also find that the similarity between natural and designed covariation is sensitive to the magnitude and mechanism of backbone flexibility used in computational protein design. Our results thus highlight the necessity of including backbone flexibility to correctly model precise details of correlated amino acid changes and give insights into the pressures underlying these correlations.
Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, California, United States of America.
Noah
N
Ollikainen
Tanja
T
Kortemme
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D000596:Amino Acids; D011506:Proteins