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; Gregory R. Bowman (Nature Communications, 2023)
Cryptic pockets enable targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. Here, the authors develop a graph neural network that accurately predicts cryptic pockets in static structures by training using molecular simulation data alone.