ArticleBioinformatics advances2025
Fast protein structure searching using structure graph embeddings.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- LoL-align: sensitive and fast probabilistic protein structure alignment.Nature methods · 2026Article
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- Systematic discovery of circular permutations across the protein universe using CIRPIN.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AlphaFind v2: similarity search in AlphaFold DB and TED domains across structural contexts.Nucleic acids research · 2026Article
- Exploring the protein universe with distant similarity detection methods.Protein science : a publication of the Protein Society · 2026Review
- CIRPIN: Learning Circular Permutation-Invariant Representations to Uncover Putative Protein Homologs.bioRxiv : the preprint server for biology · 2025Article
- Foldclass and Merizo-search: scalable structural similarity search for single- and multi-domain proteins using geometric learning.Bioinformatics (Oxford, England) · 2025Article
- CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.Nature communications · 2025Article
- Article
- Utilizing the scale-invariant feature transform algorithm to align distance matrices facilitates systematic protein structure comparison.Bioinformatics (Oxford, England) · 2024Article
- Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.Nature communications · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comparing and searching protein structures independent of primary sequence has proved useful for remote homology detection, function annotation, and protein classification. Fast and accurate methods to search with structures will be essential to make use of the vast databases that have recently become available, in the same way that fast protein sequence searching underpins much of bioinformatics. We train a simple graph neural network using supervised contrastive learning to learn a low-dimensional embedding of protein domains. Availability and implementation: The method, called Progres, is available as software at https://github.com/greener-group/progres and as a web server at https://progres.mrc-lmb.cam.ac.uk. It has accuracy comparable to the best current methods and can search the AlphaFold database TED domains in a 10th of a second per query on CPU.
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Registered trials
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.