title: Discovering sequence-structure motifs from protein segments and two applications.
abstract: We present a novel method for clustering short protein segments having strong sequence-structure correlations, and demonstrate that these clusters contain useful structural information via two applications. When applied to local tertiary structure prediction, we achieve approximately 60% accuracy with a novel dynamic programming algorithm. When applied to secondary structure prediction based on Support Vector Machines, we obtain a approximately 2% gain in Q3 performance by incorporating cluster-derived data into training and classification. These encouraging results illustrate the great potential of using conserved local motifs to tackle protein structure predictions and possibly other important problems in biology.
authors.affiliation: School of Computer Science, University of Waterloo, Ont, N2L 3G1, Canada. tcktang@cs.uwaterloo.ca
authors.forename: Thomas
authors.initials: T
authors.lastname: Tang
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
authors.forename: Ming
authors.initials: M
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology; D016013:Likelihood Functions; D018807:Polymorphism, Single-Stranded Conformational; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins