Work

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

Year: 2007

Type: article

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.

Source: Protein Science

DOI: 10.1110/ps.072856307

Authors: Marc Fasnacht, Jiang Zhu, Barry Honig

Institutions: Howard Hughes Medical Institute, Columbia University

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Institutes of Health, National Institute of General Medical Sciences

Awards: G2640710390, G4063576775, COMPUTER STUDIES OF PROTEIN STRUCTURE AND FUNCTION