Discovering sequence-structure motifs from protein segments and two applications.

Thomas Tang; Jinbo Xu; Ming Li

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PubMed · 15759642

Discovering sequence-structure motifs from protein segments and two applications.

Thomas Tang·Jinbo Xu·Ming Li

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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.

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Algorithms Amino Acid Sequence Computational Biology Likelihood Functions Polymorphism, Single-Stranded Conformational Protein Structure, Tertiary Proteins Reproducibility of Results Journal Article

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Thomas Tang, Jinbo Xu, Ming Li. 2005. Discovering sequence-structure motifs from protein segments and two applications.. https://pubmed.ncbi.nlm.nih.gov/15759642/

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