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

Local quality assessment in homology models using statistical potentials and support vector machines. Research Abstract Details 

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Local quality assessment in homology models using statistical potentials and support vector machines. Abstract Text:

Marc Fasnacht,Jiang Zhu,Barry Honig,

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.

Local quality assessment in homology models using statistical potentials and support vector machines. Publishing Authors By Initials

M Fasnacht,J Zhu,B Honig,

For similar biochemical phenomena, metabolism, and nutrition: biochemical phenomena: molecular structure: molecular conformation: protein conformation: structural homology, protein research abstracts see: biochemical phenomena, metabolism, and nutrition: biochemical phenomena: molecular structure: molecular conformation: protein conformation: structural homology, protein research

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MEDLINE DATE:

Local quality assessment in homology models using statistical potentials and support vector machines. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Protein science : a publication of the Protein Soc

VOLUME: 16

Page Numbers: 1557-68

Journal Abbreviation: Protein Sci.

ISSN: 0961-8368

DAY: 28

MONTH: 06

YEAR: 2007

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

Number of References:

LANGUAGE: eng

NlmUniqueID: 9211750

Local quality assessment in homology models using statistical potentials and support vector machines. Keywords Mesh Terms:

KEYWORDS: Structural Homology, Protein

MESH TERMS: chemistry

Chemical & Substance for Abstract: Local quality assessment in homology models using statistical potentials and support vector machines. Information

Substance Name: Proteins

Registry Number: 0

Grant and Affiliation Information for Local quality assessment in homology models using statistical potentials and support vector machines.

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

Country: United States

AGENCY: United States NIGMS

GRANT: GM-30518

ACRONYM: GM

MEDLINETA: Protein Sci

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