Development of unified statistical potentials describing protein-protein interactions

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Title: Development of unified statistical potentials describing protein-protein interactions
Author: Lu, Hui ; Lu, Long ; Skolnick, Jeffrey
Abstract: A residue-based and a heavy atom-based statistical pair potential are developed for use in assessing the strength of protein-protein interactions. To ensure the quality of the potentials, a nonredundant, high-quality dimer database is constructed. The protein complexes in this dataset are checked by a literature search to confirm that they form multimers, and the pairwise amino acid preference to interact across a protein-protein interface is analyzed and pair potentials constructed. The performance of the residue-based potentials is evaluated by using four jackknife tests and by assessing the potentials’ ability to select true protein-protein interfaces from false ones. Compared to potentials developed for monomeric protein structure prediction, the interdomain potential performs much better at distinguishing protein-protein interactions. The potential developed from homodimer interfaces is almost the same as that developed from heterodimer interfaces with a correlation coefficient of 0.92. The residue-based potential is well suited for genomic scale protein interaction prediction and analysis, such as in a recently developed threading-based algorithm, MULTIPROSPECTOR. However, the more time-consuming atom-based potential performs better in identifying near-native structures from docking generated decoys.
Description: © 2003 by the Biophysical Society
Type: Article
ISSN: 0006-3495
Citation: Lu, H, Lu L, Skolnick J. 2003. Development of unified statistical potentials describing protein-protein interactions. Biophysical journal. 84(3):1895-901.
Date: 2003-03
Contributor: Georgia Institute of Technology. Center for the Study of Systems Biology
Georgia Institute of Technology. School of Biology
Donald Danforth Plant Science Center
Publisher: Georgia Institute of Technology
Biophysical Society
Subject: Protein interactions
Genome sequencing
Pair potentials
Monomeric protein structure prediction

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