Discovering sequence-structure motifs from protein segments and two applications

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.