Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.

ABSTRACT: The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming increasingly important for the research on protein-protein interaction and drug design. For each interface residue or target residue to be predicted, the authors extract hybrid features which incorporate a wide range of information of the target residue and its spatial neighbor residues, that is, the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). Here, feature selection is performed using random forests to avoid over-fitting. Thereafter, the extreme learning machine is employed to effectively integrate these hybrid features for predicting hot spots in protein int

SUBMITTER: Wang L 

PROVIDER: S-EPMC8687429 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature