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

Artur Meller, Michael Ward, Jonathan Borowsky, Meghana Kshirsagar, Jeffrey M. Lotthammer, Felipe Oviedo, Juan Lavista Ferres and Gregory R. Bowman ()
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Artur Meller: Washington University in St. Louis
Michael Ward: Washington University in St. Louis
Jonathan Borowsky: Washington University in St. Louis
Meghana Kshirsagar: AI for Good Research Lab, Microsoft
Jeffrey M. Lotthammer: Washington University in St. Louis
Felipe Oviedo: AI for Good Research Lab, Microsoft
Juan Lavista Ferres: AI for Good Research Lab, Microsoft
Gregory R. Bowman: Washington University in St. Louis

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract 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 where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.

Date: 2023
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DOI: 10.1038/s41467-023-36699-3

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