A Multimodal Foundation Model for Contextualizing Protein Function

Abstract

While protein language models have revolutionized our understanding of molecular structures, they generally model proteins in isolation, ignoring the dynamic cellular environments that dictate their biological function. Consequently, these models struggle to capture how a protein's role shifts depending on its cellular context, a critical factor in treating complex diseases. In this thesis, we introduce SEMPER (SystEm-scale Multimodal ProtEin Representations), a unified representation learning framework that anchors context-agnostic protein sequences in context-aware cellular networks. Leveraging a dual-encoder architecture and a contrastive objective, our approach systematically aligns the representations of context-agnostic amino acid sequences and 442 cell type-specific protein interaction networks derived from a single-cell transcriptomic atlas of the adult human brain. Using this multimodal approach, we demonstrate that SEMPER systematically outperforms state-of-the-art protein language models in predicting protein function and nominating therapeutic targets across five neurodegenerative diseases in a zero-shot manner. Finally, we show that integrating SEMPER into large language models improves the reliability of de novo target discovery, laying the groundwork for context-aware reasoning of protein function.