The dependence of all-atom statistical potentials on structural training database

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/41964

Title: The dependence of all-atom statistical potentials on structural training database
Author: Zhang, Chi ; Liu, Song ; Zhou, Hongyi ; Zhou, Yaoqi
Abstract: An accurate statistical energy function that is suitable for the prediction of protein structures of all classes should be independent of the structural database used for energy extraction. Here, two high-resolution, low-sequence-identity structural databases of 333 a-proteins and 271 b-proteins were built for examining the database dependence of three all-atom statistical energy functions. They are RAPDF (residue-specific all-atom conditional probability discriminatory function), atomic KBP (atomic knowledge-based potential), and DFIRE (statistical potential based on distance-scaled finite ideal-gas reference state). These energy functions differ in the reference states used for energy derivation. The energy functions extracted from the different structural databases are used to select native structures from multiple decoys of 64 a-proteins and 28 b-proteins. The performance in native structure selections indicates that the DFIRE-based energy function is mostly independent of the structural database whereas RAPDF and KBP have a significant dependence. The construction of two additional structural databases of a/b and a 1 b-proteins further confirmed the weak dependence of DFIRE on the structural databases of various structural classes. The possible source for the difference between the three all-atom statistical energy functions is that the physical reference state of ideal gas used in the DFIRE-based energy function is least dependent on the structural database.
Description: © 2004 by the Biophysical Society
Type: Article
URI: http://hdl.handle.net/1853/41964
ISSN: 0006-3495
Citation: Zhang, C, Liu S, Zhou H, Zhou Y. 2004. The dependence of all-atom statistical potentials on structural training database. Biophysical Journal. 86(6):3349-58.
Date: 2004-03
Contributor: Georgia Institute of Technology. Center for the Study of Systems Biology
Georgia Institute of Technology. School of Biology
Howard Hughes Medical Institute. Center for Single Molecule Biophysics
State University of New York at Buffalo. Dept. of Physiology and Biophysics
Publisher: Georgia Institute of Technology
Biophysical Society
Subject: Protein structure prediction
Protein structure training databases
Protein decoys
Statistical energy functions

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