Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.
Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac330.
Protein side chains are vitally important to many biological processes such as protein-protein interaction. In this study, we evaluate the performance of our previous released side-chain modeling method OPUS-Mut, together with some other methods, on three oligomer datasets, CASP14 (11), CAMEO-Homo (65) and CAMEO-Hetero (21). The results show that OPUS-Mut outperforms other methods measured by all residues or by the interfacial residues. We also demonstrate our method on evaluating protein-protein docking pose on a dataset Oligomer-Dock (75) created using the top 10 predictions from ZDOCK 3.0.2. Our scoring function correctly identifies the native pose as the top-1 in 45 out of 75 targets. Different from traditional scoring functions, our method is based on the overall side-chain packing favorableness in accordance with the local packing environment. It emphasizes the significance of side chains and provides a new and effective scoring term for studying protein-protein interaction.
蛋白质侧链对许多生物过程至关重要,如蛋白质-蛋白质相互作用。在这项研究中,我们评估了我们之前发布的侧链建模方法 OPUS-Mut 与其他一些方法在三个寡聚体数据集(CASP14(11)、CAMEO-Homo(65)和 CAMEO-Hetero(21))上的性能。结果表明,OPUS-Mut 在所有残基或界面残基的测量中都优于其他方法。我们还使用 ZDOCK 3.0.2 的前 10 个预测结果创建的寡聚体对接数据集 Oligomer-Dock(75)来评估我们的方法。我们的打分函数正确地将天然构象识别为 75 个目标中的 45 个目标的第 1 位。与传统的打分函数不同,我们的方法是基于与局部堆积环境一致的整体侧链堆积有利程度。它强调侧链的重要性,并为研究蛋白质-蛋白质相互作用提供了一个新的有效打分项。