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Cryptic pockets are transient structural features that could provide new opportunities for drug design. However, their utility would be greatly diminished if the probability of pocket opening were universally suppressed in cells due to generic crowding effects like excluded volume. To address this concern, we study the effect of generic crowders on the opening of a cryptic pocket in the interferon inhibitory domain (IID) of Ebola viral protein 35 (eVP35) that allosterically regulates double-stranded RNA (dsRNA) binding. We find that crowding has negligible effect on dsRNA binding or the probability of cryptic pocket opening, as measured by both thiol labeling and hydrogen-deuterium exchange mass spectrometry (HDX-MS). The probability that a specific pocket opens may still be altered by specific environments. However, our results establish that crowding effects that are common to a variety of cellular environments do not pose a universal barrier to targeting cryptic pockets. Therefore, the potential of targeting cryptic pockets continues to present exciting opportunities.
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This content will become publicly available on May 23, 2027
AE-PocketMiner Uses Attention to Simultaneously Predict Cryptic Pockets and Their Allosteric Coupling
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.
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- Award ID(s):
- 2218156
- PAR ID:
- 10710822
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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