zPocket: Better Pocket Finding with Frozen Cofolding Features

Identifying protein-ligand binding sites (pockets) is important for understanding protein function and developing drugs. However, no public method has leveraged cofolding models for novel pocket prediction; furthermore, this task lacks a suitable benchmark as existing benchmarks are within the training distribution of cofolding models. We therefore developed PocketWatch, a curated dataset of biologically relevant ligand pockets. The PocketWatch test set consists of unseen pockets stratified by sequence similarity, structural similarity, and crypticity to enable evaluation of generalization to novel and challenging pockets. Using this dataset, we develop zPocket, a method that trains pocket-prediction heads on top of frozen features from cofolding models. We show that zPocket heads outperform baseline methods across all stratifications. Our best zPocket model achieves overall per-target AUPRC of 0.757 compared to 0.626 for the strongest baseline, VN-EGNN, retrained on the PocketWatch split (0.544 when using the published weights); the widely used P2Rank reaches 0.503. We further show that zPocket can be used to outperform baseline methods on top-$N+2$ recall of predicted pocket centroids through clustering per-residue predictions or using zPocket to rescore centroids proposed by other models. Finally, we explore two applications of zPocket: crystallographic fragment screens and a proteome-scale scan for uncharacterized pockets on human proteins. Together these results suggest that zPocket heads built on cofolding trunk features can greatly advance ligand binding site prediction.