AE-PocketMiner Uses Attention to Simultaneously Predict Cryptic Pockets and Their Allosteric Coupling
Year: 2026
Type: preprint
Abstract: Finding and targeting cryptic pockets could dramatically expand the druggable proteome. However, discovering these sites remains challenging since they are only open a fraction of the time. It is also difficult to predict the functional relevance of a cryptic site as this often requires insight into allostery. Here we introduce attention enabled (AE-)PocketMiner, an artificial intelligence (AI) method that uses a graph neural network with an attention mechanism to simultaneously predict the locations of cryptic pockets and their allosteric coupling to the rest of the protein from a single input structure. We show that AE-PocketMiner outperforms past methods for identifying cryptic pockets and recapitulates known allosteric interactions. Moreover, we experimentally confirm newly predicted cryptic pockets and mutations that allosterically control pocket opening. AE-PocketMiner thus provides a powerful framework for multiple steps of the drug discovery process-including pocket identification, prioritization, and assay design-that will help expand the druggable proteome.
Institution: University of Pennsylvania
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G6822323257
OpenAlex ID: W7162211506