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Local quality assessment in homology models using statistical potentials and support vector machines
Author(s) -
Fasnacht Marc,
Zhu Jiang,
Honig Barry
Publication year - 2007
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.072856307
Subject(s) - computer science , support vector machine , superposition principle , similarity (geometry) , statistical model , machine learning , quality (philosophy) , local structure , artificial intelligence , homology (biology) , data mining , mathematics , biology , physics , mathematical analysis , quantum mechanics , image (mathematics) , chemical physics , biochemistry , gene
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.