Huang Yuanpeng J, Mao Binchen, Aramini James M, Montelione Gaetano T
Center for Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854; Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854.
Proteins. 2014 Feb;82 Suppl 2(0 2):43-56. doi: 10.1002/prot.24488.
Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.
基于模板的建模(TBM)是蛋白质结构预测关键评估(CASP)的一个主要组成部分。在CASP10中,150个预测小组提交的约41740个预测模型被评估为基于模板的建模预测。通过使用包含全局比对指标和基于距离矩阵的指标的综合评分,与实验性X射线晶体结构和核磁共振结构进行几何比较,来评估蛋白质结构预测的准确性。这些指标包括GDT-HA和GDC-all全局比对分数,以及基于叠加无关的LDDT距离矩阵分数。此外,一种类似于先前用于将蛋白质模型与实验性核磁共振数据进行比较的叠加无关的RPF指标,被用于将预测的蛋白质结构模型与实验性蛋白质结构进行比较。为了在所有这四个指标上取得好成绩,模型必须对主链和侧链构象都做出准确的预测。分别针对服务器以及服务器与人工整理的预测小组的组合确定性能排名。最终排名是使用每个类别中表现最佳的前25个小组的原始指标分数进行配对双样本t检验分析得出的。