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One of the most challenging and long-standing problems in computationalbiology is the prediction of three-dimensional protein structure from aminoacid sequence. A promising approach to infer spatial proximity between residuesis the study of evolutionary covariance from multiple sequence alignments,especially in light of recent algorithmic improvements and the fast growingsize of sequence databases. In this paper, we present a simple, fast andaccurate algorithm for the prediction of residue-residue contacts based onregularized least squares. The basic assumption is that spatially proximalresidues in a protein coevolve to maintain the physicochemical complementarityof the amino acids involved in the contact. Our regularized inversion of thesample covariance matrix allows the computation of partial correlations betweenpairs of residues, thereby removing the effect of spurious transitivecorrelations. The method also accounts for low number of observations by meansof a regularization parameter that depends on the effective number of sequencesin the alignment. When tested on a set of protein families from Pfam, we foundthe RLS algorithm to have performance comparable to state-of-the-art methodsfor contact prediction, while at the same time being faster and conceptuallysimpler.

Prediction of residue-residue contacts from protein families using similarity kernels and least squares regularization