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Open Access Research article

A pairwise residue contact area-based mean force potential for discrimination of native protein structure

Shahriar Arab1, Mehdi Sadeghi23*, Changiz Eslahchi4, Hamid Pezeshk5 and Armita Sheari1

Author Affiliations

1 Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

2 National Institute of Genetic Engineering and Biotechnology, Tehran-Karaj Highway, Tehran, Iran

3 School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

4 Department of Mathematics and Center of Excellence in Algebraic and Logical Structures in Discrete Mathematics, Shahid Beheshti University, Tehran, Iran

5 School of Mathematics, Statistics and Computer Sciences, Center of Excellence in Biomathematics, College of Science, University of Tehran, Tehran, Iran

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BMC Bioinformatics 2010, 11:16  doi:10.1186/1471-2105-11-16

Published: 9 January 2010



Considering energy function to detect a correct protein fold from incorrect ones is very important for protein structure prediction and protein folding. Knowledge-based mean force potentials are certainly the most popular type of interaction function for protein threading. They are derived from statistical analyses of interacting groups in experimentally determined protein structures. These potentials are developed at the atom or the amino acid level. Based on orientation dependent contact area, a new type of knowledge-based mean force potential has been developed.


We developed a new approach to calculate a knowledge-based potential of mean-force, using pairwise residue contact area. To test the performance of our approach, we performed it on several decoy sets to measure its ability to discriminate native structure from decoys. This potential has been able to distinguish native structures from the decoys in the most cases. Further, the calculated Z-scores were quite high for all protein datasets.


This knowledge-based potential of mean force can be used in protein structure prediction, fold recognition, comparative modelling and molecular recognition. The program is available at webcite