Proteins perform their functions by interacting with other biomolecules. For these interactions, proteins often form homo- or hetero-oligomers as well. Thus, oligomer protein structures provide important clues regarding the biological roles of proteins. To this end, computational prediction of oligomer structures may be a useful tool in the absence of experimentally resolved structures. Here, we describe our server and human-expert methods used to predict oligomer structures in the CASP14 experiment. Examples are provided for cases in which manual domain-splitting led to improved oligomeric domain structures by ab initio docking, automated oligomer structure refinement led to improved subunit orientation and terminal structure, and manual oligomer modeling utilizing literature information generated a reasonable oligomer model. We also discussed the results of post-prediction docking calculations with AlphaFold2 monomers as input in comparison to our blind prediction results. Overall, ab initio docking of AlphaFold2 models did not lead to better oligomer structure prediction, which may be attributed to the interfacial structural difference between the AlphaFold2 monomer structures and the crystal oligomer structures. This result poses a next-stage challenge in oligomer structure prediction after the success of AlphaFold2. For successful protein assembly structure prediction, a different approach that exploits further evolutionary information on the interface and/or flexible docking taking the interfacial conformational flexibilities of subunit structures into account is needed.

中文翻译：

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在 CASP14 中使用 GALAXY 进行蛋白质寡聚体结构预测

蛋白质通过与其他生物分子相互作用来发挥其功能。对于这些相互作用，蛋白质通常也形成同源或异源寡聚体。因此，寡聚体蛋白质结构提供了关于蛋白质生物学作用的重要线索。为此，在没有实验解析结构的情况下，低聚物结构的计算预测可能是一种有用的工具。在这里，我们描述了在 CASP14 实验中用于预测寡聚体结构的服务器和人类专家方法。提供了以下示例：手动域拆分通过从头算对接改进了寡聚域结构，自动寡聚体结构细化导致亚基方向和末端结构改进，以及利用文献信息的手动寡聚体建模产生了合理的寡聚体模型。与我们的盲预测结果相比，我们还讨论了以 AlphaFold2 单体作为输入的预测后对接计算结果。总体而言，AlphaFold2 模型的从头算对接并没有带来更好的低聚物结构预测，这可能是由于 AlphaFold2 单体结构和晶体低聚物结构之间的界面结构差异所致。这一结果对 AlphaFold2 成功后的低聚物结构预测提出了下一阶段的挑战。对于成功的蛋白质组装结构预测，需要一种不同的方法，利用界面上的进一步进化信息和/或考虑亚基结构的界面构象灵活性的灵活对接。