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Local quality assessment in homology models using statistical potentials and support vector machines

Howard Hughes Medical Institute and Columbia University, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics Columbia University, New York, New York 10032, USA

(RECEIVED March 8, 2007; FINAL REVISION May 4, 2007; ACCEPTED May 6, 2007)

In this study, we address the problem of local quality assessment in homology models. As a prerequisite for the evaluation of methods for predicting local model quality, we first examine the problem of measuring local structural similarities between a model and the corresponding native structure. Several local geometric similarity measures are evaluated. Two methods based on structural superposition are found to best reproduce local model quality assessments by human experts. We then examine the performance of state-of-the-art statistical potentials in predicting local model quality on three qualitatively distinct data sets. The best statistical potential, DFIRE, is shown to perform on par with the best current structure-based method in the literature, ProQres. A combination of different statistical potentials and structural features using support vector machines is shown to provide somewhat improved performance over published methods.

Keywords: homology modeling; protein model; model evaluation; similarity measure; statistical potential; support vector machine; protein structure prediction

Supplemental material: see www.proteinscience.org

Reprint requests to: Barry Honig, Howard Hughes Medical Institute and Columbia University, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics Columbia University, 1130 St. Nicholas Avenue, Room 815, New York, NY 10032, USA; e-mail: bh6{at}columbia.edu; fax: (212) 851-4650.

Article published online ahead of print. Article and publication date are at http://www.proteinscience.org/cgi/doi/10.1110/ps.072856307.

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