美国哈佛医学院Marinka Zitnik团队近期取得重要工作进展，他们研究开发了单细胞蛋白质生物学的情境人工智能模型。相关研究成果2024年7月22日在线发表于《自然—方法学》杂志上。

据介绍，了解蛋白质功能和开发分子疗法需要破译蛋白质在其中起作用的细胞类型以及蛋白质之间的相互作用。然而，对现有算法来说，在生物背景下模拟蛋白质相互作用仍然具有挑战性。

研究人员介绍PINNACLE，这是一种几何深度学习方法。利用多器官单细胞图谱，PINNACLE在蛋白质相互作用网络上学习，从24个组织的156个细胞类型背景中产生394760个蛋白质表征。PINNACLE的嵌入空间反映了细胞和组织，使组织层次结构的零样本检索成为可能。

预训练的蛋白质表征可以适应下游任务：增强基于3D结构的表征，以解决免疫肿瘤蛋白质相互作用，并研究药物对不同细胞类型的影响。PINNACLE在指定类风湿性关节炎和炎症性肠病的治疗靶点方面优于最先进的模型，并且比无背景模型具有更高的预测能力。

总之，PINNACLE根据其操作环境调整其输出的能力为生物学中的大规模特定环境预测作出了铺垫。

附：英文原文

Title: Contextual AI models for single-cell protein biology

Author: Li, Michelle M., Huang, Yepeng, Sumathipala, Marissa, Liang, Man Qing, Valdeolivas, Alberto, Ananthakrishnan, Ashwin N., Liao, Katherine, Marbach, Daniel, Zitnik, Marinka

Issue&Volume: 2024-07-22

Abstract: Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE’s embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs’ effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE’s ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

DOI: 10.1038/s41592-024-02341-3

Source: https://www.nature.com/articles/s41592-024-02341-3