用于蛋白质-蛋白质相互作用建模的深度学习方法生态系统日益壮大

Julia R Rogers, Gergö Nikolényi, Mohammed AlQuraishi
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引用次数: 0

摘要

许多细胞功能都依赖于蛋白质之间的相互作用。然而,由于蛋白质组中采用的分子识别机制多种多样,全面描述它们的工作仍然面临挑战。深度学习通过利用蛋白质相互作用的实验数据和基本生物物理知识,已成为解决这一问题的一种有前途的方法。在此,我们回顾了用于蛋白质相互作用建模的深度学习方法日益增长的生态系统,强调了这些生物物理知识模型的多样性及其各自的权衡。我们讨论了最近在利用表征学习捕捉与预测蛋白质相互作用和相互作用位点相关的复杂特征、利用几何深度学习推理蛋白质结构和预测复杂结构以及利用生成模型设计全新蛋白质组装方面取得的成功。我们还概述了一些突出的挑战和有希望的新方向。发现新的相互作用、阐明其物理机制、利用深度学习设计粘合剂以调节其功能,以及最终揭示蛋白质相互作用如何协调复杂的细胞行为的机会比比皆是。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Growing ecosystem of deep learning methods for modeling protein–protein interactions
Numerous cellular functions rely on protein–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.
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