Ability of a Structural World Model to Detect Cryptic Pockets from Apo Structure

Abstract

Cryptic pockets are druggable sites that are absent in a protein's resting structure and form only upon backbone rearrangement, posing a challenge for detection from static apo structures. Existing methods rely on generating open conformations through sampling or complex prediction, limiting applicability. Here we present a novel detector that reads cryptic pockets directly from a structural world-model latent representation of a single apo structure, without conformational sampling or external pocket finders. Evaluated on CryptoBench and CryptoBank datasets, the method localizes cryptic sites with top-1 accuracy as high as 0.848 and top-5 accuracy exceeding 0.93, and successfully recovers an allosteric site on held-out WRN helicase structures. The detector complements existing approaches and improves with training data scale. These findings suggest that structural world-model latents encode conformational flexibility, enabling effective cryptic pocket identification from apo structures alone, facilitating drug discovery on challenging targets. Benchmarked against the co-folding engine OpenDDE, the detector finds pockets directly rather than by first predicting a bound complex: on 190 targets held out of both training sets it recovers 119 sites OpenDDE misses, against 2 in the other direction, and at top-5 co-folding's recovered sites are a subset of ours. It runs on any structure from the apo coordinates alone, including the mmCIF-only entries all recent depositions carry, and its accuracy keeps climbing as the training corpus grows. The intended use is prospective cryptic-site nomination on the targets that sequence and static structure leave without a starting point.