Assessment of model accuracy estimations in CASP12.
Assessment of model accuracy estimations in CASP12.
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DOI：
10.1002/prot.25371
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发表时间：
2018-03
期刊：
影响因子：
2.9
通讯作者：
Tramontano A
中科院分区：
文献类型：
--
作者：
Kryshtafovych A;Monastyrskyy B;Fidelis K;Schwede T;Tramontano A
CASP12 测试了创纪录的 42 种模型精度估计方法。本文介绍了这些方法在整个模型和每个残留精度模式下的评估结果。来自四个不同模型评估包的分数被用作评估方法估计准确性的“基本事实”。它们包括刚体分数 - GDT_TS 和三个基于局部结构的分数 - LDDT、CAD 和 SphereGrinder。评估了方法从多个可用模型中识别最佳模型、预测模型的绝对精度分数、区分好模型和坏模型、预测坐标误差自估计的精度以及区分模型中可靠和不可靠区域的能力。单模型方法在从诱饵集中挑选最佳模型方面已发展到比聚类方法更好的地步。另一方面，共识方法利用同一目标蛋白的大量模型的可用性，在区分好模型和坏模型以及预测模型的局部准确性方面仍然更好。结果表明，最佳精度估计方法相对于时间冻结参考聚类方法和先前 CASP 相应类方法中最佳方法的结果表现更好。当作为模型选择器进行评估时，表现最佳的单模型方法比除三个 CASP12 三级结构预测器之外的所有方法都表现得更好。
The record high 42 model accuracy estimation methods were tested in CASP12. The paper presents results of the assessment of these methods in the whole-model and per-residue accuracy modes. Scores from four different model evaluation packages were used as the ‘ground truth’ for assessing accuracy of methods’ estimates. They include a rigid-body score - GDT_TS, and three local-structure based scores - LDDT, CAD and SphereGrinder. The ability of methods to identify best models from among several available, predict model’s absolute accuracy score, distinguish between good and bad models, predict accuracy of the coordinate error self-estimates, and discriminate between reliable and unreliable regions in the models was assessed. Single-model methods advanced to the point where they are better than clustering methods in picking the best models from decoy sets. On the other hand, consensus methods, taking advantage of the availability of large number of models for the same target protein, are still better in distinguishing between good and bad models and predicting local accuracy of models. The best accuracy estimation methods were shown to perform better with respect to the frozen in time reference clustering method and the results of the best method in the corresponding class of methods from the previous CASP. Top performing single-model methods were shown to do better than all but three CASP12 tertiary structure predictors when evaluated as model selectors.