Contextual AI Models for Single-Cell Protein Biology - Zitnik Lab
Protein interaction networks are a critical component in studying the function and therapeutic potential of proteins. However, accurately modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms.
We introduce PINNACLE, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-PINNACLE protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, PINNACLE provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs.
We demonstrate that PINNACLE's contextualized representations of proteins reflect cellular and tissue organization and PINNACLE's tissue representations enable zero-shot retrieval of the tissue hierarchy. Infused with cellular and tissue contexts, PINNACLE's protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations (namely, PD-1/PD-L1 and B7-1/CTLA-4) and to study the genomic effects of drugs across cellular contexts. Enabled by contextualized learning, PINNACLE's protein representations outperform state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases in at least 18.6% (29 out of 156) and 8.6% (13 out of 152) of cell type contexts, respectively. PINNACLE empowers the long-standing paradigm of incorporating biological context into artificial intelligence models to better model biological systems.
Proteins are the functional units of cells, and their interactions allow performing different biological functions. The development of high-throughput methods has enabled the characterization of large maps of protein interactions. Leveraging these protein interaction networks, computational methods have been developed to improve the understanding of protein structure, accurately predict functional annotations, and inform the design of therapeutic targets.
