How well can we predict native contacts in proteins based on decoy structures and their energies?
One strategy for ab initio protein structure prediction is to generate a large number of possible structures (decoys) and select the most fitting ones based on a scoring or free energy function. The conformational space of a protein is huge, and chances are rare that any heuristically generated structure will directly fall in the neighborhood of the native structure. It is desirable that, instead of being thrown away, the unfitting decoy structures can provide insights into native structures so prediction can be made progressively. First, we demonstrate that a recently parameterized physics-based effective free energy function based on the GROMOS96 force field and a generalized Born/surface area solvent model is, as several other physics-based and knowledge-based models, capable of distinguishing native structures from decoy structures for a number of widely used decoy databases. Second, we observe a substantial increase in correlations of the effective free energies with the degree of similarity between the decoys and the native structure, if the similarity is measured by the content of native inter-residue contacts in a decoy structure rather than its root-mean-square deviation from the native structure. Finally, we investigate the possibility of predicting native contacts based on the frequency of occurrence of contacts in decoy structures. For most proteins contained in the decoy databases, a meaningful amount of native contacts can be predicted based on plain frequencies of occurrence at a relatively high level of accuracy. Relative to using plain frequencies, overwhelming improvements in sensitivity of the predictions are observed for the 4_state_reduced decoy sets by applying energy-dependent weighting of decoy structures in determining the frequency. There, approximately 80% native contacts can be predicted at an accuracy of approximately 80% using energy-weighted frequencies. The sensitivity of the plain frequency approach is much lower (20% to 40%). Such improvements are, however, not observed for the other decoy databases. The rationalization and implications of the results are discussed.
Zhu
Jiang
J
Key Laboratory of Structural Biology, University of Science and Technology of China, Chinese Academy of Sciences, School of Life Sciences, Hefei, Anhui, 230026, China. jz2106@columbia.edu
Zhu
Qianqian
Q
Shi
Yunyu
Y
Liu
Haiyan
H
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013816:Thermodynamics
D011506:Proteins