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

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, C(α), 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 C(α )RMSD) targets in the test set were successfully identified. The average C(α )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.