The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. Electronic address: https://twitter.com/ben_shor.
The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
Curr Opin Struct Biol. 2024 Aug;87:102841. doi: 10.1016/j.sbi.2024.102841. Epub 2024 May 24.
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.
基于深度学习的蛋白质结构预测的最新进展彻底改变了结构生物学领域。它不仅能够高通量地预测蛋白质-蛋白质相互作用的结构，还能够预测单一蛋白质的结构。尽管在预测复杂结构方面取得了成功，但对于大型的高分子组装体仍然需要专门的方法。在这里，我们描述了使用集成和分层方法来构建高分子组装体模型的最新进展。我们强调了一些应用，这些应用可以预测蛋白质-蛋白质相互作用，并根据相互作用网络来建模复合物，包括预测复合物的化学计量和异质性。
