Physical interactions between proteins are essential for most biological processes governing life¹. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein–protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications^{2,3,4,5,6,7,8,9}. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein–protein interactions¹⁰. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

蛋白质之间的物理相互作用对于大多数控制生命的生物过程至关重要¹ 。然而，即使基因组、蛋白质组和结构数据不断增加，这种相互作用的分子决定因素仍然难以理解。这种知识差距一直是全面理解细胞蛋白质-蛋白质相互作用网络以及蛋白质结合剂从头设计的主要障碍，而蛋白质结合剂对于合成生物学和转化应用至关重要^{2,3,4,5,6,7,8 ,9} .在这里，我们使用在蛋白质表面运行的几何深度学习框架，生成指纹来描述对于驱动蛋白质-蛋白质相互作用至关重要的几何和化学特征¹⁰ 。我们假设这些指纹捕获了分子识别的关键方面，代表了新型蛋白质相互作用的计算设计的新范例。作为原理证明，我们通过计算设计了几种从头蛋白质结合剂来接合四种蛋白质靶标：SARS-CoV-2 刺突、PD-1、PD-L1 和 CTLA-4。几种设计经过实验优化，而其他设计则纯粹在计算机中生成，达到纳摩尔亲和力，结构和突变表征显示出高度准确的预测。总体而言，我们以表面为中心的方法捕获了分子识别的物理和化学决定因素，从而为蛋白质相互作用以及更广泛的具有功能的人造蛋白质的从头设计提供了一种方法。