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Numerous cellular functions rely on protein$\unicode{x2013}$proteininteractions. Efforts to comprehensively characterize them remain challengedhowever by the diversity of molecular recognition mechanisms employed withinthe proteome. Deep learning has emerged as a promising approach for tacklingthis problem by exploiting both experimental data and basic biophysicalknowledge about protein interactions. Here, we review the growing ecosystem ofdeep learning methods for modeling protein interactions, highlighting thediversity of these biophysically-informed models and their respectivetrade-offs. We discuss recent successes in using representation learning tocapture complex features pertinent to predicting protein interactions andinteraction sites, geometric deep learning to reason over protein structuresand predict complex structures, and generative modeling to design de novoprotein assemblies. We also outline some of the outstanding challenges andpromising new directions. Opportunities abound to discover novel interactions,elucidate their physical mechanisms, and engineer binders to modulate theirfunctions using deep learning and, ultimately, unravel how protein interactionsorchestrate complex cellular behaviors.

Growing ecosystem of deep learning methods for modeling protein$\unicode{x2013}$protein interactions