One of the most challenging and long-standing problems in computational biology is the prediction of three-dimensional protein structure from amino acid sequence. A promising approach to infer spatial proximity between residues is the study of evolutionary covariance from multiple sequence alignments, especially in light of recent algorithmic improvements and the fast growing size of sequence databases. In this paper, we present a simple, fast and accurate algorithm for the prediction of residue-residue contacts based on regularized least squares. The method incorporates in a very natural manner amino acid similarity in the calculation of covariance, and accounts for low number of observations by a regularization parameter that depends on the effective number of sequences in the alignment. Most importantly, inversion of the sample covariance matrix allows the computation of partial correlations between pairs of residues, thereby removing the effect of spurious transitive correlations.  When tested on a set of protein families from PFAM , we found the RLS algorithm to have superior performance compared to PSICOV, a state-of-the-art method for contact prediction  .
One of the most challenging and long-standing problems in computational biology is the prediction of three-dimensional protein structure from amino acid sequence. A promising approach to infer spatial proximity between residues is the study of evolutionary covariance from multiple sequence alignments, especially in light of recent algorithmic improvements and the fast growing size of sequence databases. In this paper, we present a simple, fast and accurate algorithm for the prediction of residue-residue contacts based on regularized least squares. The basic assumption is that spatially proximal residues in a protein coevolve to maintain the physicochemical complementarity of the amino acids involved in the contact. Our regularized inversion of the sample covariance matrix allows the computation of partial correlations between pairs of residues, thereby removing the effect of spurious transitive correlations. The method also accounts for low number of observations by means of a regularization parameter that depends on the effective number of sequences in the alignment. When tested on a set of protein families from Pfam , we found the RLS algorithm to have performance comparable to state-of-the-art methods for contact prediction , while at the same time being faster and conceptually simpler .
R
method incorporates in a very natural manner amino acid similarity in the calculation of covariance, and accounts for low number of observations by a regularization parameter that depends on the effective number of sequences in the alignment. Most importantly,
basic assumption is that spatially proximal residues in a protein coevolve to maintain the physicochemical complementarity of the amino acids involved in the contact. Our regularized
A
The method also accounts for low number of observations by means of a regularization parameter that depends on the effective number of sequences in the alignment.
R
PFAM
Pfam
R
superior performance compared to PSICOV, a
performance comparable to
R
method
methods
A
, while at the same time being faster and conceptually simpler