The other 90% of the protein: Assessment beyond the Cαs for CASP8 template-based and high-accuracy models
The other 90% of the protein: Assessment beyond the Cαs for CASP8 template-based and high-accuracy models
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DOI：
10.1002/prot.22551
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发表时间：
2009-01-01
影响因子：
2.9
通讯作者：
Richardson, Jane S.
中科院分区：
文献类型：
--
作者：
Keedy, Daniel A.;Williams, Christopher J.;Richardson, Jane S.
对于 CASP8 蛋白质结构预测技术关键评估中基于模板的建模，这项工作开发并应用了六个新的全模型指标。它们旨在通过全局距离测试 (GDT) 和相关分数（基于目标结构和标记为“模型 1”的预测之间 C α 原子的多重叠加）来补充和增加传统基于模板的评估的价值。新指标使用具有高于平均 GDT 的最佳模型的所有原子来评估每个目标的每个预测变量组。有两个指标评估预测模型的“蛋白质相似度”：用于验证实验结构的 MolProbity 评分，以及使用全原子空间冲突、键长和角度异常值以及主链二面体的主链现实评分。其他四个新指标评估模型与主链和侧链氢键、侧链末端定位和侧链旋转异构体目标的匹配度。六个全模型度量的组平均 Z 分数与组平均 GDT Z 分数进行平均，以产生全模型、高精度性能的总体排名。针对预测组性能的特定方面报告单独的评估，例如近似正确的模板或折叠识别的稳健性，以及识别最佳模型的自我评分能力。如果考虑到目标难度，折叠识别与组平均 GDT Z 分数不同但相关，而自我评分最好由服务器完成，与 GDT 性能不相关。确定并讨论了针对特定目标的杰出个体模型。预测器组在不同方面表现出色，凸显了当前方法的多样性。然而，良好的全模型分数与高 C alpha 准确度密切相关。
For template-based modeling in the CASP8 Critical Assessment of Techniques for Protein Structure Prediction, this work develops and applies six new full-model metrics. They are designed to complement and add value to the traditional template-based assessment by the global distance test (GDT) and related scores (based on multiple superpositions of C alpha atoms between target structure and predictions labeled "Model 1"). The new metrics evaluate each predictor group on each target, using all atoms of their best model with above-average GDT. Two metrics evaluate how "protein-like" the predicted model is: the MolProbity score used for validating experimental structures, and a mainchain reality score using all-atom steric clashes, bond length and angle outliers, and backbone dihedrals. Four other new metrics evaluate match of model to target for mainchain and side-chain hydrogen bonds, side-chain end positioning, and side-chain rotamers. Group-average Z-score across the six full-model measures is averaged with group-average GDT Z-score to produce the overall ranking for full-model, high-accuracy performance. Separate assessments are reported for specific aspects of predictor-group performance, such as robustness of approximately correct template or fold identification, and self-scoring ability at identifying the best of their models. Fold identification is distinct from but correlated with group-average GDT Z-score if target difficulty is taken into account, whereas self-scoring is done best by servers and is uncorrelated with GDT performance. Outstanding individual models on specific targets are identified and discussed. Predictor groups excelled at different aspects, highlighting the diversity of current methodologies. However, good full-model scores correlate robustly with high C alpha accuracy.