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

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 or complex insertions & deletions patterns, and operates independently of the downstream modeling objective. We introduce Protriever, an end-to-end differentiable framework that learns to retrieve relevant homologs while simultaneously training for the target task. When applied to protein fitness prediction, Protriever achieves state-of-the-art performance compared to sequence-based models that rely on MSA-based homolog retrieval, while being two orders of magnitude faster through efficient vector search. Protriever is both architecture- and task-agnostic, and can flexibly adapt to different retrieval strategies and protein databases at inference time -- offering a scalable alternative to alignment-centric approaches.

@article{weitzman2025_2506.08954,
  title={ Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction },
  author={ Ruben Weitzman and Peter Mørch Groth and Lood Van Niekerk and Aoi Otani and Yarin Gal and Debora Marks and Pascal Notin },
  journal={arXiv preprint arXiv:2506.08954},
  year={ 2025 }
}