ArticleBioinformatics (Oxford, England)2026
Multi-scale structural similarity embedding search across entire proteomes.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions.iScience · 2026Article
- RCSB protein data Bank: Next-generation advanced search for exploration of experimental structures and computed structure models.Protein science : a publication of the Protein Society · 2026Article
- AlphaFind v2: similarity search in AlphaFold DB and TED domains across structural contexts.Nucleic acids research · 2026Article
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6 authors.
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Abstract
motivationThe rapid expansion of three-dimensional (3D) biomolecular structure information, driven by breakthroughs in artificial intelligence/deep learning (AI/DL)-based structure predictions, has created an urgent need for scalable and efficient structure similarity search methods. Traditional alignment-based approaches, such as structural superposition tools, are computationally expensive and challenging to scale with the vast number of available macromolecular structures.
resultsHerein, we present a scalable structure similarity search strategy designed to navigate extensive repositories of experimentally determined structures and computed structure models predicted using AI/DL methods. Our approach leverages protein language models and a deep neural network architecture to transform 3D structures into fixed-length vectors, enabling efficient large-scale comparisons. Although trained to predict TM-scores between single-domain structures, our model generalizes beyond the domain level, accurately identifying 3D similarity for full-length polypeptide chains and multimeric assemblies. By integrating vector databases, our method facilitates efficient large-scale structure retrieval, addressing the growing challenges posed by the expanding volume of 3D biostructure information. AVAILABILITY AND IMPLEMENTATION: Source code available at https://github.com/bioinsilico/rcsb-embedding-search. Source code DOI: https://doi.org/10.6084/m9.figshare.30546698.v1. Benchmark datasets DOI: https://doi.org/10.6084/m9.figshare.30546650.v1. Web server prototype available at: http://embedding-search.rcsb.org/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.