LoCo: a novel main chain scoring function for protein structure prediction based on local coordinates

被引:7
|
作者
Moughon, Stewart E. [1 ]
Samudrala, Ram [1 ]
机构
[1] Univ Washington, Dept Microbiol, Seattle, WA 98195 USA
来源
BMC BIOINFORMATICS | 2011年 / 12卷
关键词
QUASI-CHEMICAL APPROXIMATION; DEAD-END ELIMINATION; ENERGY FUNCTIONS; PAIR POTENTIALS; MEAN FORCE; X-RAY; SECONDARY STRUCTURE; CONTACT POTENTIALS; GLOBULAR-PROTEINS; FOLDED PROTEINS;
D O I
10.1186/1471-2105-12-368
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Successful protein structure prediction requires accurate low-resolution scoring functions so that protein main chain conformations that are close to the native can be identified. Once that is accomplished, a more detailed and time-consuming treatment to produce all-atom models can be undertaken. The earliest low-resolution scoring used simple distance-based "contact potentials," but more recently, the relative orientations of interacting amino acids have been taken into account to improve performance. Results: We developed a new knowledge-based scoring function, LoCo, that locates the interaction partners of each individual residue within a local coordinate system based only on the position of its main chain N, C(alpha) and C atoms. LoCo was trained on a large set of experimentally determined structures and optimized using standard sets of modeled structures, or "decoys." No structure used to train or optimize the function was included among those used to test it. When tested against 29 other published main chain functions on a group of 77 commonly used decoy sets, our function outperformed all others in C(alpha) RMSD rank of the best-scoring decoy, with statistically significant p-values < 0.05 for 26 out of the 29 other functions considered. LoCo is fast, requiring on average less than 6 microseconds per residue for interaction and scoring on commonly-used computer hardware. Conclusions: Our function demonstrates an unmatched combination of accuracy, speed, and simplicity and shows excellent promise for protein structure prediction. Broader applications may include protein-protein interactions and protein design.
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页数:14
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