Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction

Abstract

Retrieving homologous protein sequences is essential for a broad range of protein modeling tasks such as fitness prediction, protein design, structure modeling, and protein-protein interactions. Traditional workflows have relied on a two-step process: first retrieving homologs via Multiple Sequence Alignments (MSA), then training models on one or more of these alignments. However, MSA-based retrieval is computationally expensive, struggles with highly divergent sequences and complex insertions/deletions, and operates independently of downstream modeling. We introduce Protriever, an end-to-end differentiable framework that unifies retrieval and task modeling. Focusing on protein fitness prediction, we show that Protriever achieves performance on par with the most sensitive MSA-based tools while being orders of magnitude faster at retrieval, as it relies on efficient vector search. Protriever is both architecture- and task-agnostic, and can flexibly adapt to different retrieval strategies and protein databases at inference -- offering a scalable alternative to alignment-centric approaches.