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

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 ？ 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.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.Protein structure evaluation is a key process in protein structure prediction, in association with comparative modeling, fold recognition, structure refinement, and de novo folding. Protein design technology also requires structure evaluation methods with sufficient capacity. Many different types of potential energy functions have been developed and examined. The formulation of the functions can be roughly grouped under physical-based and knowledge-based approaches [1,2,4]. Physical-based (or molecular mechanics) potential energy functions are mainly used for the simulation of protein folding and dynamics [2], and are also effective for protein design [3]. The knowledge-based approach to developing such an evaluation system is also effective and widely used, especially for protein structure prediction and prot