Nature of protein family signatures: Insights from singular value analysis of position-specific scoring matrices

TL;DR

This study applies singular value decomposition to PSSMs to uncover the underlying signatures of protein families, revealing correlations with amino acid properties and potential for functional site prediction.

Contribution

It introduces a novel analysis method using SVD on PSSMs to distinguish functionally important residues from structurally important ones in proteins.

Findings

First singular vectors correlate with amino acid indices and site conservation.

Second singular vectors relate to hydrophobicity and contact numbers.

Method can help predict functional sites in proteins.

Abstract

Position-specific scoring matrices (PSSMs) are useful for detecting weak homology in protein sequence analysis, and they are thought to contain some essential signatures of the protein families. In order to elucidate what kind of ingredients constitute such family-specific signatures, we apply singular value decomposition to a set of PSSMs and examine the properties of dominant right and left singular vectors. The first right singular vectors were correlated with various amino acid indices including relative mutability, amino acid composition in protein interior, hydropathy, or turn propensity, depending on proteins. A significant correlation between the first left singular vector and a measure of site conservation was observed. It is shown that the contribution of the first singular component to the PSSMs act to disfavor potentially but falsely functionally important residues at conserved sites. The second right singular vectors were highly correlated with hydrophobicity scales, and the corresponding left singular vectors with contact numbers of protein structures. It is suggested that sequence alignment with a PSSM is essentially equivalent to threading supplemented with functional information. The presented method may be used to separate functionally important sites from structurally important ones, and thus it may be a useful tool for predicting protein functions.