title: Fast and accurate multi-class protein fold recognition with spatial sample kernels.
abstract: Establishing structural or functional relationship between sequences, for instance to infer the structural class of an unannotated protein, is a key task in biological sequence analysis. Recent computational methods such as profile and neighborhood mismatch kernels have shown very promising results for protein sequence classification, at the cost of high computational complexity. In this study we address the multi-class sequence classification problems using a class of string-based kernels, the sparse spatial sample kernels (SSSK), that are both biologically motivated and efficient to compute. The proposed methods can work with very large databases of protein sequences and show substantial improvements in computing time over the existing methods. Application of the SSSK to the multi-class protein prediction problems (fold recognition and remote homology detection) yields significantly better performance than existing state-of-the-art algorithms.
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA. pkuksa@cs.rutgers.edu
authors.forename: Pavel
authors.initials: P
authors.lastname: Kuksa
authors.forename: Pai-Hsi
authors.initials: PH
authors.lastname: Huang
authors.forename: Vladimir
authors.initials: V
authors.lastname: Pavlovic
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins