title: Predicting protein-protein interactions from sequences in a hybridization space.
abstract: To understand the networks in living cells, it is indispensably important to identify protein-protein interactions on a genomic scale. Unfortunately, it is both time-consuming and expensive to do so solely based on experiments due to the nature of the problem whose complexity is obviously overwhelming, just like the fact that "life is complicated". Therefore, developing computational techniques for predicting protein-protein interactions would be of significant value in this regard. By fusing the approach based on the gene ontology and the approach of pseudo-amino acid composition, a predictor called "GO-PseAA" predictor was established to deal with this problem. As a showcase, prediction was performed on 6323 protein pairs from yeast. To avoid redundancy and homology bias, none of the protein pairs investigated has > or = 40% sequence identity with any other. The overall success rate obtained by jackknife cross-validation was 81.6%, indicating the GO-PseAA predictor is very promising for predicting protein-protein interactions from protein sequences, and might become a useful vehicle for studying the network biology in the postgenomic era.
authors.affiliation: Gordon Life Science Institute, 13784 Torrey Del Mar, San Diego, California 92130, USA.
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D005656:Fungal Proteins / Q000378:metabolism*; D011485:Protein Binding; D020539:Sequence Analysis, Protein
chemical_list: D000596:Amino Acids; D005656:Fungal Proteins