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Open Access Research article

A knowledge-based structure-discriminating function that requires only main-chain atom coordinates

Yoshihide Makino* and Nobuya Itoh

Author Affiliations

Department of Biotechnology, Faculty of Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu-shi, Toyama 939-0398, Japan

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BMC Structural Biology 2008, 8:46  doi:10.1186/1472-6807-8-46

Published: 29 October 2008

Abstract

Background

The use of knowledge-based potential function is a powerful method for protein structure evaluation. A variety of formulations that evaluate single or multiple structural features of proteins have been developed and studied. The performance of functions is often evaluated by discrimination ability using decoy structures of target proteins. A function that can evaluate coarse-grained structures is advantageous from many aspects, such as relatively easy generation and manipulation of model structures; however, the reduction of structural representation is often accompanied by degradation of the structure discrimination performance.

Results

We developed a knowledge-based pseudo-energy calculating function for protein structure discrimination. The function (Discriminating Function using Main-chain Atom Coordinates, DFMAC) consists of six pseudo-energy calculation components that deal with different structural features. Only the main-chain atom coordinates of N, Cα, and C atoms for the respective amino acid residues are required as input data for structure evaluation. The 231 target structures in 12 different types of decoy sets were separated into 154 and 77 targets, and function training and the subsequent performance test were performed using the respective target sets. Fifty-nine (76.6%) native and 68 (88.3%) near-native (< 2.0 Å Cα RMSD) targets in the test set were successfully identified. The average Cα RMSD of the test set resulted in 1.174 with the tuned parameters. The major part of the discrimination performance was supported by the orientation-dependent component.

Conclusion

Despite the reduced representation of input structures, DFMAC showed considerable structure discrimination ability. The function can be applied to the identification of near-native structures in structure prediction experiments.