ArticleGenome research2024
Protein domain embeddings for fast and accurate similarity search.
Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- A novel transformer model of protein domains for viral taxonomy classification.Bioinformatics (Oxford, England) · 2026Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- Exploring the protein universe with distant similarity detection methods.Protein science : a publication of the Protein Society · 2026Review
- NEAR: neural embeddings for amino acid relationships.Bioinformatics (Oxford, England) · 2025Article
- NEAR: Neural Embeddings for Amino acid Relationships.bioRxiv : the preprint server for biology · 2025Article
- Major advances in protein function assignment by remote homolog detection with protein language models - A review.Current opinion in structural biology · 2025Review
- Fast protein structure searching using structure graph embeddings.Bioinformatics advances · 2025Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
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3 authors.
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Abstract
Recently developed protein language models have enabled a variety of applications with the protein contextual embeddings they produce. Per-protein representations (each protein is represented as a vector of fixed dimension) can be derived via averaging the embeddings of individual residues, or applying matrix transformation techniques such as the discrete cosine transformation (DCT) to matrices of residue embeddings. Such protein-level embeddings have been applied to enable fast searches of similar proteins; however, limitations have been found; for example, PROST is good at detecting global homologs but not local homologs, and knnProtT5 excels for proteins with single domains but not multidomain proteins. Here, we propose a novel approach that first segments proteins into domains (or subdomains) and then applies the DCT to the vectorized embeddings of residues in each domain to infer domain-level contextual vectors. Our approach, called DCTdomain, uses predicted contact maps from ESM-2 for domain segmentation, which is formulated as a
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