From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction.

ABSTRACT: Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of

SUBMITTER: Cocco S 

PROVIDER: S-EPMC3749948 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature