Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and whe...

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Detalles Bibliográficos
Autores principales: Meller, Artur, Ward, Michael, Borowsky, Jonathan, Kshirsagar, Meghana, Lotthammer, Jeffrey M., Oviedo, Felipe, Ferres, Juan Lavista, Bowman, Gregory R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977097/
https://www.ncbi.nlm.nih.gov/pubmed/36859488
http://dx.doi.org/10.1038/s41467-023-36699-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977097/
https://www.ncbi.nlm.nih.gov/pubmed/36859488
http://dx.doi.org/10.1038/s41467-023-36699-3

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