PROTOCOL: Late Interaction Retrieval for Protein Homolog Search

• URL: http://arxiv.org/abs/2605.29158v1
• Date: Wed, 27 May 2026 22:50:48 GMT
• Title: PROTOCOL: Late Interaction Retrieval for Protein Homolog Search
• Abstract summary: ProtoCol is a model which represents proteins as sets of residue embeddings.<n>It encodes proteins independently, keeps candidate representations pre-computable, and scores candidates with MaxSim over residue embeddings.<n>On SCOPe superfamily and Pfam clan benchmarks, ProtoCol outperforms sequence-composition, alignment-based, pooled PLM, and trained single-vector baselines.
• Score: 8.797956246984388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ProtLingo: Efficient Protein Language Modeling via Conditional Memory and Expert Routing [39.80134572747517]
  Protein language models (PLMs) provide a scalable approach for modeling sequence-function relationships from unlabeled sequences.<n>We introduce ProtLingo, an efficient PLM framework that augments a pretrained single-sequence backbone with conditional local memory and sparse expert routing.<n>ProtLingo achieves competitive performance with a 150M-scale backbone, including strong parameter efficiency on mutation-effect prediction.
  arXiv  Detail & Related papers  (2026-09-04T06:46:42Z)
• Structuring Semantic Embeddings for Principle Evaluation: A Prototype-Guided Contrastive Learning Approach [58.15427952222424]
  General-purpose text embeddings are widely deployed for such tasks, but broad semantic similarity can place semantically similar yet task-distinct examples.<n>We introduce Prototype-Guided Contrastive Learning (PGCL), a prototype-guided geometric regularization module built on top of frozen text embeddings.
  arXiv  Detail & Related papers  (2026-08-15T13:19:25Z)
• Protein Thoughts: Interpretable Reasoning with Tree of Thoughts and Embedding-Space Flow Matching for Protein-Protein Interaction Discovery [9.70057752523548]
  We present textbfProtein Thoughts, a framework that reformulates PPI discovery as an interpretable search problem with explicit reasoning.<n>Protein Thoughts achieves mean best-binder rank of 11.2 versus 47.7 for an entropic tree search baseline, a 76% improvement, and for binding prediction the trained value function achieves $91.08 pm 0.19$ Micro-F1.
  arXiv  Detail & Related papers  (2026-05-19T04:14:06Z)
• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [88.98041407783502]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms [2.399426243085768]
  We propose HIPPO, a hierarchical contrastive framework for protein-protein interaction prediction.<n>The proposed approach incorporates hierarchical contrastive loss functions that emulate the structured relationship among functional classes of proteins.<n> Experiments on benchmark datasets demonstrate that HIPPO achieves state-of-the-art performance, outperforming existing methods and showing robustness in low-data regimes.
  arXiv  Detail & Related papers  (2025-07-03T15:41:04Z)
• Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction [28.150437140009025]
  Protriever is an end-to-end differentiable framework that learns to retrieve relevant homologs while simultaneously training for the target task.<n>We introduce Protriever, an end-to-end differentiable framework that learns to retrieve relevant homologs while simultaneously training for the target task.
  arXiv  Detail & Related papers  (2025-06-10T16:24:09Z)
• A PLMs based protein retrieval framework [3.878994100846403]
  We propose a novel protein retrieval framework that mitigates the bias towards sequence similarity.<n>Our framework initiatively harnesses protein language models (PLMs) to embed protein sequences within a high-dimensional feature space.<n>Extensive experiments demonstrate that our framework can equally retrieve both similar and dissimilar proteins.
  arXiv  Detail & Related papers  (2024-07-16T09:52:42Z)
• PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction [63.50967073653953]
  Compound-Protein Interaction prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures. We propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction.
  arXiv  Detail & Related papers  (2024-02-13T03:51:10Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.