ArticleNature biotechnology2025
Fast, sensitive detection of protein homologs using deep dense retrieval.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- A trimodal protein language model enables advanced protein searches.Nature biotechnology · 2026Article
- Benchmarking protein sequence and structure search methods for remote homology detection.Genome biology · 2026Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Structural genomics sheds light on protein functions and remote homologs across the insect tree of life.Cell research · 2026Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- Scalable homology detection with ERAST.Nature biotechnology · 2026Article
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Deep homology and design of proteasome chaperone proteins in Candidozyma auris.Nature communications · 2026Article
- A novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.BMC biology · 2026Article
- Uncovering evolutionarily remote and highly potent antimicrobial peptides with protein language models.Nature biomedical engineering · 2026Article
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- TheInternational journal of hypertension · 2026Article
- Exploring the protein universe with distant similarity detection methods.Protein science : a publication of the Protein Society · 2026Review
- Protein design drives synthetic biology research of plant natural products.Biodesign research · 2025Review
- Boosting AlphaFold protein tertiary structure prediction through MSA engineering and extensive model sampling and ranking in CASP16.Communications biology · 2025Article
- Evaluating the significance of embedding-based protein sequence alignment with clustering and double dynamic programming for remote homology.Scientific reports · 2025Article
- DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models.Bioinformatics (Oxford, England) · 2025Article
- An interpretable alphabet for local protein structure search based on amino acid neighborhoods.Bioinformatics (Oxford, England) · 2025Article
- Aligning large language models and geometric deep models for protein representation.Patterns (New York, N.Y.) · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.
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