In CASP14, 39 research groups submitted more than 2500 3D models on 22 protein complexes. In general, the community performed well in predicting the fold of the assemblies (for 80% of the targets), although it faced significant challenges in reproducing the native contacts. This is especially the case for the complexes without whole-assembly templates. The leading predictor, BAKER-experimental, used a methodology combining classical techniques (template-based modeling, protein docking) with deep learning-based contact predictions and a fold-and-dock approach. The Venclovas team achieved the runner-up position with template-based modeling and docking. By analyzing the target interfaces, we showed that the complexes with depleted charged contacts or dominating hydrophobic interactions were the most challenging ones to predict. We also demonstrated that if AlphaFold2 predictions were at hand, the interface prediction challenge could be alleviated for most of the targets. All in all, it is evident that new approaches are needed for the accurate prediction of assemblies, which undoubtedly will expand on the significant improvements in the tertiary structure prediction field.

中文翻译：

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CASP14 组装预测的评估


在 CASP14 中，39 个研究小组提交了 22 种蛋白质复合物的 2500 多个 3D 模型。总体而言，该社区在预测组件折叠（80% 的目标）方面表现良好，尽管在重现本地接触方面面临着重大挑战。对于没有整体装配模板的复合体尤其如此。领先的预测器 BAKER-experimental 使用了一种将经典技术（基于模板的建模、蛋白质对接）与基于深度学习的接触预测和折叠对接方法相结合的方法。 Venclovas团队凭借基于模板的建模和对接获得了亚军的位置。通过分析目标界面，我们发现具有耗尽电荷接触或占主导地位的疏水相互作用的复合物是最具挑战性的预测。我们还证明，如果 AlphaFold2 预测可用，那么大多数目标的界面预测挑战都可以得到缓解。总而言之，显然需要新的方法来准确预测组件，这无疑将扩展三级结构预测领域的重大改进。