Structure-based predictive models for allosteric hot spots.

ABSTRACT: In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray. The resulting models performed well on an independent data set consisting of hotspots and non-hotspo

SUBMITTER: Demerdash ON 

PROVIDER: S-EPMC2748687 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature