Propensity vectors of low-ASA residue pairs in the distinction of protein interactions.
We introduce low-ASA residue pairs as classification features for distinguishing the different types of protein interactions. A low-ASA residue pair is defined as two contact residues each from one chain that have a small solvent accessible surface area (ASA). This notion of residue pairs is novel as it first combines residue pairs with the O-ring theory, an influential proposition stating that the binding hot spots at the interface are often surrounded by a ring of energetically less important residues. As binding hot spots lie in the core of the stability for protein interactions, we believe that low-ASA residue pairs can sharpen the distinction of protein interactions. The main part of our feature vector is 210-dimensional, consisting of all possible low-ASA residue pairs; the value of every feature is determined by a propensity measure. Our classification method is called OringPV, which uses propensity vectors of protein interactions for support vector machine. OringPV is tested on three benchmark datasets for a variety of classification tasks such as the distinction between crystal packing and biological interactions, the distinction between two different types of biological interactions, etc. The evaluation frameworks include within-dataset, cross-dataset comparison, and leave-one-out cross-validation. The results show that low-ASA residue pairs and the propensity vector description of protein interactions are truly strong in the distinction. In particular, many cross-dataset generalization capability tests have achieved excellent recalls and overall accuracies, much outperforming existing benchmark methods.
Liu
Qian
Q
Bioinformatics Research Center & School of Computer Engineering, Nanyang Technological University, Singapore 639798.
Li
Jinyan
J
D000465:Algorithms; D008956:Models, Chemical; D008958:Models, Molecular; D011487:Protein Conformation; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000378:metabolism; D012372:ROC Curve; D018709:Statistics, Nonparametric; D013499:Surface Properties
D011506:Proteins