Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
Biomolecular Interactions, Max Planck Institute for Developmental Biology, 72076, Tübingen, Germany.
Nat Commun. 2021 Mar 2;12(1):1396. doi: 10.1038/s41467-021-21636-z.
Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.
在高通量实验中已经鉴定出越来越多的蛋白质相互作用，但只有一小部分已经解决了结构问题。最近，基于序列共进化的方法在预测单体蛋白质结构和蛋白质相互作用界面方面取得了突破。在这里，我们通过快速对齐串联方法和残基相互作用的概率评分来解决大规模相互作用在残基分辨率上的预测挑战。重要的是，正如我们在这里应用于大规模实验数据集（其中未知成对相互作用）所示，该方法（EVcomplex2）能够评估蛋白质相互作用的可能性。我们在大肠杆菌膜蛋白组中预测了 504 个新的相互作用，其中包括 243 个新发现的相互作用。虽然 EVcomplex2 不需要可用的结构，但共进化的残基对可用于生成蛋白质相互作用的结构模型，我们在这里对包括鞭毛钩-丝状体连接和 Tol/Pal 复合物在内的膜复合物进行了这样的操作。
