Ovchinnikov Sergey, Park Hahnbeom, Kim David E, Liu Yuan, Wang Ray Yu-Ruei, Baker David
Department of Biochemistry, University of Washington, Seattle, Washington, 98195.
Institute for Protein Design, University of Washington, Seattle, Washington, 98195.
Proteins. 2016 Sep;84 Suppl 1(Suppl 1):181-8. doi: 10.1002/prot.25006. Epub 2016 Mar 6.
In CASP11 we generated protein structure models using simulated ambiguous and unambiguous nuclear Overhauser effect (NOE) restraints with a two stage protocol. Low resolution models were generated guided by the unambiguous restraints using continuous chain folding for alpha and alpha-beta proteins, and iterative annealing for all beta proteins to take advantage of the strand pairing information implicit in the restraints. The Rosetta fragment/model hybridization protocol was then used to recombine and regularize these models, and refine them in the Rosetta full atom energy function guided by both the unambiguous and the ambiguous restraints. Fifteen out of 19 targets were modeled with GDT-TS quality scores greater than 60 for Model 1, significantly improving upon the non-assisted predictions. Our results suggest that atomic level accuracy is achievable using sparse NOE data when there is at least one correctly assigned NOE for every residue. Proteins 2016; 84(Suppl 1):181-188. © 2016 Wiley Periodicals, Inc.
在蛋白质结构预测技术关键评估第11轮(CASP11)中,我们使用模拟的模糊和明确核Overhauser效应(NOE)约束,通过两阶段方案生成蛋白质结构模型。低分辨率模型通过明确约束进行引导生成,对于α和α-β蛋白采用连续链折叠,对于所有β蛋白采用迭代退火,以利用约束中隐含的链配对信息。然后使用Rosetta片段/模型杂交方案对这些模型进行重组和规整,并在明确和模糊约束的引导下,利用Rosetta全原子能量函数对其进行优化。19个目标中有15个的模型1的全局距离测试-总分(GDT-TS)质量得分大于60,相比无辅助预测有显著提高。我们的结果表明,当每个残基至少有一个正确分配的NOE时,使用稀疏的NOE数据可实现原子水平的准确性。《蛋白质》2016年;第84卷增刊1:181 - 188页。©2016年威利期刊出版公司。