Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Genome Center, University of California, Davis, California, USA.
Proteins. 2021 Dec;89(12):1673-1686. doi: 10.1002/prot.26172. Epub 2021 Jul 23.
This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.
这份报告描述了第 14 轮结构预测关键评估(CASP14)中对建模困难目标的三级结构预测评估。我们实施了一个官方排名方案,该方案使用与之前基于拓扑结构的 CASP 评估相同的分数,但将这些分数与强调物理现实模型的分数相结合。在本次评估中考虑的所有困难目标中,除了两个目标之外,表现最好的 AlphaFold2 小组在所有目标上的表现都优于预测社区的其他成员。他们为大多数目标(超过 GDT_TS 70 的 86%)提供了高质量的模型,包括超过 150 个残基的较大目标,并且几乎正确预测了其余所有目标的拓扑结构。AlphaFold2 的表现紧随其后的是两种手动 Baker 方法、一种改进 Zhang-server 模型的 Feig 方法、两种著名的自动化 Zhang server 方法(QUARK 和 Zhang-server),以及一种 Zhang 手动小组。尽管在困难目标的蛋白质结构预测方面取得了显著进展,但预测社区和 AlphaFold2(在较小程度上)都面临着与柔性区域和必需寡聚体组装相关的挑战。官方排名方案的制定是基于 PCA 和热图聚类的性能,这些性能为目标难度和最成功的最新结构预测方法提供了深入的了解。