# PROTOCOL: Late Interaction Retrieval for Protein Homolog Search

arXiv: https://arxiv.org/abs/2605.29158 · HTML: https://mlsanity.com/paper/2605.29158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [PLAID: An Efficient Engine for Late Interaction Retrieval](https://mlsanity.com/paper/2205.09707/) (2022)
- [Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering](https://mlsanity.com/paper/2309.17133/) (2023)
- [Rethinking the Role of Token Retrieval in Multi-Vector Retrieval](https://mlsanity.com/paper/2304.01982/) (2023)
- [PyLate: Flexible Training and Retrieval for Late Interaction Models](https://mlsanity.com/paper/2508.03555/) (2025)
- [TARGET: Benchmarking Table Retrieval for Generative Tasks](https://mlsanity.com/paper/2505.11545/) (2025)
- [DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries](https://mlsanity.com/paper/2210.15748/) (2022)
- [SPLATE: Sparse Late Interaction Retrieval](https://mlsanity.com/paper/2404.13950/) (2024)

## Abstract (arXiv)

Protein homology search underlies function annotation, structure prediction, and evolutionary analysis, but remains challenging in the "twilight zone," where global sequence similarity is weak and classical alignment methods lose sensitivity. Protein language models provide context-aware representations that could improve alignment sensitivity in this regime. However, prior protein embedding-based retrieval pipelines often pool these representations into a single vector, potentially obscuring local motifs, domains, or conserved residues that reveal remote homology. We introduce ProtoCol, a model which represents proteins as sets of residue embeddings and uses ColBERT-style late interaction to test whether residue-level comparison improves homolog retrieval. ProtoCol encodes proteins independently, keeps candidate representations pre-computable, and scores candidates with MaxSim over residue embeddings. On SCOPe superfamily and Pfam clan benchmarks, ProtoCol outperforms sequence-composition, alignment-based, pooled PLM, and trained single-vector baselines, supporting late interaction as an effective retrieval layer for remote homology search.