CSM-potential: mapping PrOTEin iNteracTIons And biological Ligands in 3D space using geometric deep learning
View / Open Files
Authors
Journal Title
Nucleic Acids Research
ISSN
0305-1048
Publisher
Oxford University Press
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Ascher, D. CSM-potential: mapping PrOTEin iNteracTIons And biological Ligands in 3D space using geometric deep learning. Nucleic Acids Research https://doi.org/10.17863/CAM.84214
Abstract
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.
Embargo Lift Date
2025-05-06
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.84214
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336795
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk