这篇来自诺奖得主David Baker组最新的Science研究论文介绍了预测人类蛋白质-蛋白质相互作用（PPI）的系统性方法，旨在克服现有实验和计算方法的局限性。研究人员通过两个关键创新显著提高了预测精度和效率：首先，他们利用7倍更深的多序列比对（MSAs），直接从大量未组装的真核基因组数据中获取更强的共进化信号，创建了omicMSA。其次，他们开发了一种名为RoseTTAFold2-PPI (RF2-PPI)的快速深度学习网络，该网络利用从AlphaFold数据库中提取的结构域-结构域相互作用（DDI）数据进行训练，训练数据集规模扩大了17倍。通过结合RF2-PPI的快速筛选能力和AlphaFold2的准确三维建模，研究团队系统筛选了超过1.91亿个人类蛋白质对，最终预测出17,849个高置信度PPI，其中包括3,631个先前未知的相互作用，特别是在跨膜蛋白中，这些发现为理解人类疾病的分子机制提供了新的见解。References: Zhang J, Humphreys I R, Pei J, et al. Predicting protein-protein interactions in the human proteome[J]. Science, 2025: eadt1630.
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