Numerous cellular functions rely on protein$\unicode{x2013}$protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.

翻译：大量细胞功能依赖于蛋白质间的相互作用。然而，由于蛋白质组内分子识别机制的多样性，全面表征这些相互作用仍然面临挑战。深度学习通过利用实验数据及蛋白质相互作用的基本生物物理知识，已成为解决该问题的有前景方法。本文综述了用于建模蛋白质相互作用的深度学习方法的成长生态，重点阐述了这些基于生物物理信息的模型的多样性及其各自的权衡。我们讨论了近期在以下方面的成功应用：利用表示学习捕捉与预测蛋白质相互作用及相互作用位点相关的复杂特征、利用几何深度学习推演蛋白质结构并预测复合物结构、以及利用生成建模从头设计蛋白质组装体。同时，我们概述了当前面临的突出挑战与值得探索的新方向。大量机遇蕴藏于发现新型相互作用、阐明其物理机制、设计调节功能的蛋白质结合物——这些均可借助深度学习实现，并最终揭示蛋白质相互作用如何协调复杂的细胞行为。