Abstract
BackgroundThe 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.ResultsWe 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.ConclusionDespite 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.
Highlights
The use of knowledge-based potential function is a powerful method for protein structure evaluation
The structure-discriminating function developed in this study consists of six pseudoenergy calculation components
The pseudo-energy is calculated based on the Boltzmann law [5], with knowledge-based procedures using a precompiled database from a non-redundant set of known structures
Summary
The use of knowledge-based potential function is a powerful method for protein structure evaluation. The performance of functions is often evaluated by discrimination ability using decoy structures of target proteins. Physical-based (or molecular mechanics) potential energy functions are mainly used for the simulation of protein folding and (page number not for citation purposes). The knowledge-based approach to developing such an evaluation system is effective and widely used, especially for protein structure prediction and protein design studies [1]. The best average Z-score was obtained for lattice_ssfit (10.499), and the worst for hg_structal (1.762). The average C.C.decoy of 4state_reduced (0.767), hg_structal (0.800) and moulder (0.821) were relatively high. The worst average C.C.decoy (0.000) was obtained for lattice_ssfit. The average F.E.decoy of 4state_reduced (61.5%) and moulder (61.9%) were significant, and the worst was for ig_structal_hires (0.0%)
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