Department of Biophysics, UT Southwestern Medical Center, Dallas, Texas, USA.
Howard Hughes Medical Institute, UT Southwestern Medical Center, Dallas, Texas, USA.
Proteins. 2021 Dec;89(12):1700-1710. doi: 10.1002/prot.26225. Epub 2021 Sep 15.
The high accuracy of some CASP14 models at the domain level prompted a more detailed evaluation of structure predictions on whole targets. For the first time in critical assessment of structure prediction (CASP), we evaluated accuracy of difficult domain assembly in models submitted for multidomain targets where the community predicted individual evaluation units (EUs) with greater accuracy than full-length targets. Ten proteins with domain interactions that did not show evidence of conformational change and were not involved in significant oligomeric contacts were chosen as targets for the domain interaction assessment. Groups were ranked using complementary interaction scores (F1, QS score, and Jaccard coefficient), and their predictions were evaluated for their ability to correctly model inter-domain interfaces and overall protein folds. Target performance was broadly grouped into two clusters. The first consisted primarily of targets containing two EUs wherein predictors more broadly predicted domain positioning and interfacial contacts correctly. The other consisted of complex two-EU and three-EU targets where few predictors performed well. The highest ranked predictor, AlphaFold2, produced high-accuracy models on eight out of 10 targets. Their interdomain scores on three of these targets were significantly higher than all other groups and were responsible for their overall outperformance in the category. We further highlight the performance of AlphaFold2 and the next best group, BAKER-experimental on several interesting targets.
一些 CASP14 模型在结构域水平上的高精度促使我们更详细地评估针对整个靶标的结构预测。这是首次在结构预测的关键评估(CASP)中,我们评估了针对多结构域靶标提交的模型中困难结构域组装的准确性,在这些靶标中,社区对单个评估单元(EU)的预测准确性高于全长靶标。选择了 10 个具有无构象变化证据且不涉及显著寡聚接触的结构域相互作用的蛋白质作为结构域相互作用评估的靶标。使用互补相互作用评分(F1、QS 评分和 Jaccard 系数)对这些组进行排名,并评估它们正确模拟结构域界面和整体蛋白质折叠的能力。靶标性能大致分为两个集群。第一个主要由包含两个 EU 的靶标组成,其中预测器更广泛地正确预测结构域定位和界面接触。另一个由复杂的双 EU 和三 EU 靶标组成,其中很少有预测器表现良好。排名最高的预测器 AlphaFold2 在 10 个靶标中的 8 个靶标上生成了高精度模型。它们在其中三个靶标上的结构域间评分明显高于所有其他组,这是它们在该类别中总体表现优异的原因。我们进一步强调了 AlphaFold2 和排名第二的 BAKER-experimental 在几个有趣的靶标上的性能。