Moeder, K. E. | bioRxiv Science

bioRxiv2020

Discovery of a cryptic allosteric site in Ebola's 'undruggable' VP35 protein using simulations and experiments

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola viral protein 35 (VP35). VP35 plays multiple essential roles in Ebolas replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these successes, we expect our pipeline is equally applicable to other proteins.

biochemistry↗

bioRxiv2018

Exposons exploit cooperative changes in solvent exposure to detect cryptic allosteric sites and other functionally-relevant conformational transitions

Proteins are dynamic molecules that undergo conformational changes to a broad spectrum of different excited states. Unfortunately, the small populations of these states make it difficult to determine their structures or functional implications. Computer simulations are an increasingly powerful means to identify and characterize functionally-relevant excited states. However, this advance has uncovered a further challenge: it can be extremely difficult to identify the most salient features of large simulation datasets. We reasoned that many functionally-relevant conformational changes are likely to involve large, cooperative changes to the surfaces that are available to interact with potential binding partners. To examine this hypothesis, we introduce a method that returns a prioritized list of potentially functional conformational changes by segmenting protein structures into clusters of residues that undergo cooperative changes in their solvent exposure, along with the hierarchy of interactions between these groups. We term these groups exposons to distinguish them from other types of clusters that arise in this analysis and others. We demonstrate, using three different model systems, that this method identifies experimentally-validated and functionally-relevant conformational changes, including conformational switches, allosteric coupling, and cryptic pockets. Our results suggest that key functional sites are hubs in the network of exposons. As a further test of the predictive power of this approach, we apply it to discover cryptic allosteric sites in two different {beta}-lactamase enzymes that are widespread sources of antibiotic resistance. Experimental tests confirm our predictions for both systems. Importantly, we provide the first evidence for a cryptic allosteric site in CTX-M-9 {beta}-lactamase. Experimentally testing this prediction did not require any mutations, and revealed that this site exerts the most potent allosteric control over activity of any pockets found in {beta}-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 {beta}-lactamase demonstrates the utility of exposons.

biophysics↗