2007
Type(s)Journal Article
AuthorsFasnacht, M., Zhu, J., Honig, B.
SourceProtein Science 2007 16:1557-1568
Urlhttp://dx.doi.org/10.1110/ps.072856307
Abstract
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.
Technology Platform
Computational Biology
Research Topics
Protein Structure Prediction