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

Abstract

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.