A Generalized Framework for Protein Sequence Annotation

Huzefa Rangwala, Christopher Kauffman, and George Karypis
UMN TR# 07-023, 2007
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Over the last decade several data mining techniques have been developed for determining structural and functional properties of individual protein residues using sequence and sequence-derived information. These protein residue annotation problems are often formulated as either classification or regression problems and solved using a common set of techniques.

We develop a generalized protein sequence annotation toolkit (ProSAT) for solving classification or regression problems using support vector machines. The key characteristic of our method is its effective use of window-based information to capture the local environment of a protein sequence residue. This window information is used with several kernel functions available within our framework. We show the effectiveness of using the previously developed normalized second order exponential kernel function and experiment with local window-based information at different levels of granularity.

We report empirical results on a diverse set of classification and regression problems: prediction of solvent accessibility, secondary structure, local structure alphabet, transmembrane helices, DNA-protein interaction sites, contact order, and regions of disorder are all explored. Our methods show either comparable or superior results to several state-of-the-art application tuned prediction methods for these problems. ProSAT provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems. The results of some of these predictions can be used to assist in solving the overarching 3D structure prediction problem.

A short version of this paper appears at the NIPS Machine Learning and Computational Biology Workshop, 2008.
Research topics: Bioinformatics | MONSTER | ProSAT | Protein structure prediction