Context-Aware Protein Representations Using Protein Language Models and Optimal Transport

Proteins have different functions in different contexts. As a result, representations that take into account a proteins biological context would allow for a more accurate assessment of its functions and properties. Protein language models (PLMs) generate amino-acid-level (residue-level) embeddings of proteins and are a powerful approach for creating universal protein representations. However, PLMs on their own do not consider context and cannot generate context-specific protein representations. We introduce COPTER, a method that uses optimal transport to pool together a proteins PLM-generated residue-level embeddings using a separate context embedding to create context-aware protein representations. We conceptualize the residue-level embeddings as samples from a probabilistic distribution, and use sliced Wasserstein distances to map these samples against a context-specific reference set, yielding a contextualized protein-level embedding. We evaluate COPTERs performance on three downstream prediction tasks: therapeutic drug target prediction, genetic perturbation response prediction, and TCR-epitope binding prediction. Compared to state-of-the-art baselines, COPTER achieves substantially improved, near-perfect performance in predicting therapeutic targets across cell contexts. It also results in improved performance in predicting responses to genetic perturbations and binding between TCRs and epitopes. The implementation code is available at https://github.com/SahilP113/COPTER.