Year: 2024
Type: article
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.
Source: Bioinformatics Advances
Authors: Joe G Greener, Kiarash Jamali
Institutions: MRC Laboratory of Molecular Biology, Medical Research Council
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,917
Awards: G2026990854, G3401413002
OpenAlex ID: W4409217924