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Published online before print June 28, 2007, 10.1110/ps.072856307
Protein Science (2007), 16:1557-1568. Published by Cold Spring Harbor Laboratory Press. Copyright © 2007 The Protein Society
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Local quality assessment in homology models using statistical potentials and support vector machines

Marc Fasnacht1,, Jiang Zhu, and Barry Honig

Howard Hughes Medical Institute at 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


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