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 data sets. 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 b-lactamase enzymes that are widespread sources of antibiotic resistance. Experimental tests confirm our predictions for both systems. Importantly, we provide the first evidence, to our knowledge, for a cryptic allosteric site in CTX-M-9 b-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 b-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 b-lactamase demonstrates the utility of exposons.

Allosteric drugs, which bind to proteins in regions other than their main ligand-binding or active sites, make it possible to target proteins considered “undruggable” and to develop new therapies that circumvent existing resistance. Despite growing interest in allosteric drug discovery, rational design is limited by a lack of sufficient structural information about alternative binding sites in proteins. Previously, we used Markov State Models (MSMs) to identify such “cryptic pockets,” and here we describe a method for identifying compounds that bind in these cryptic pockets and modulate enzyme activity. Experimental tests validate our approach by revealing both an inhibitor and two activators of TEM β-lactamase (TEM). To identify hits, a library of compounds is first virtually screened against either the crystal structure of a known cryptic pocket or an ensemble of structures containing the same cryptic pocket that is extracted from an MSM. Hit compounds are then screened experimentally and characterized kinetically in individual assays. We identify three hits, one inhibitor and two activators, demonstrating that screening for binding to allosteric sites can result in both positive and negative modulation. The hit compounds have modest effects on TEM activity, but all have higher affinities than previously identified inhibitors, which bind the same cryptic pocket but were found, by chance, via a computational screen targeting the active site. Site-directed mutagenesis of key contact residues predicted by the docking models is used to confirm that the compounds bind in the cryptic pocket as intended. Because hit compounds are identified from docking against both the crystal structure and structures from the MSM, this platform should prove suitable for many proteins, particularly targets whose crystal structures lack obvious druggable pockets, and for identifying both inhibitory and activating small-molecule modulators.

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 virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola’s 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 results, we expect this pipeline will be applicable to other proteins.

Many proteins are classified as 'undruggable,' especially those that engage in proteinprotein and protein-nucleic acid interactions. Discovering 'cryptic' pockets that are absent in available structures but open due to protein dynamics could provide new druggable sites. Here, we integrate atomically-detailed simulations and biophysical experiments to search for cryptic pockets in viral protein 35 (VP35) from the highly lethal Ebola virus. VP35 plays multiple essential roles in Ebola's replication cycle, including binding the viral RNA genome to block a host's innate immunity. However, VP35 has so far proved undruggable. Using adaptive sampling simulations and allosteric network detection algorithms, we uncover a cryptic pocket that is allosterically coupled to VP35's key RNA-binding interface. Experimental tests corroborate the predicted pocket and confirm that stabilizing the open form allosterically disrupts RNA binding. These results demonstrate simulations' power to characterize hidden conformations and dynamics, uncovering cryptic pockets and allostery that present new therapeutic opportunities.

Conformational changes can dramatically alter a protein's function by changing the surfaces that are accessible to interact with binding partners. However, it is often difficult to hone in on the most relevant conformational changes from the cartesian coordinates of atoms on the protein's surface. Instead, we describe a protein's surface in terms of groups of residues that undergo cooperative changes in their solvent exposure. We term these groups exposons. We demonstrate that Markov state models (MSMs) elegantly identify the conformational transitions that give rise to an exposon, enabling users to rapidly find the most interesting conformational changes in their system. For example, this approach readily identifies previously-known cryptic allosteric sites and other functionally-relevant conformational transitions. Moreover, it predicts a cryptic allosteric site in an important target for combating antibiotic resistance that lacks known cryptic pockets. Experimental tests confirm that targeting this site reduces catalytic efficiency 15-fold.