Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships.
One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more proteins whose structure or function is already known. Toward this end, we propose a means of representing proteins using pairwise sequence similarity scores. This representation, combined with a discriminative classification algorithm known as the support vector machine (SVM), provides a powerful means of detecting subtle structural and evolutionary relationships among proteins. The algorithm, called SVM-pairwise, when tested on its ability to recognize previously unseen families from the SCOP database, yields significantly better performance than SVM-Fisher, profile HMMs, and PSI-BLAST.
Liao
Li
L
Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716, USA.
Noble
William Stafford
WS
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D040681:Structural Homology, Protein
D011506:Proteins