Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites

Abstract

Cryptic allosteric sites-transient pockets in a folded protein that are invisible to conventional experiments but can alter enzymatic activity via allosteric communication with the active site-are a promising opportunity for facilitating drug design by greatly expanding the repertoire of available drug targets. Unfortunately, identifying these sites is difficult, typically requiring resourceintensive screening of large libraries of small molecules. Here, we demonstrate that Markov state models built from extensive computer simulations (totaling hundreds of microseconds of dynamics) can identify prospective cryptic sites from the equilibrium fluctuations of three medically relevant proteins-β-lactamase, interleukin-2, and RNase H-even in the absence of any ligand. As in previous studies, our methods reveal a surprising variety of conformations-including bound-like configurations-that implies a role for conformational selection in ligand binding. Moreover, our analyses lead to a number of unique insights. First, direct comparison of simulations with and without the ligand reveals that there is still an important role for an induced fit during ligand binding to cryptic sites and suggests new conformations for docking. Second, correlations between amino acid sidechains can convey allosteric signals even in the absence of substantial backbone motions. Most importantly, our extensive sampling reveals a multitude of potential cryptic sites-consisting of transient pockets coupled to the active site-even in a single protein. Based on these observations, we propose that cryptic allosteric sites may be even more ubiquitous than previously thought and that our methods should be a valuable means of guiding the search for such sites. molecular dynamics | native state dynamics T he degree to which standard, rational drug design protocols ignore protein-conformational changes is a potentially major flaw. Most assume that proteins are frozen in their crystallographic structures because we do not understand the intrinsic dynamics of proteins well enough to include them. Once this assumption is made, the only way to manipulate a protein's activity is with inhibitors that bind more tightly to the protein's active site than the molecule's natural ligand. Unfortunately, only approximately 15% of proteins have a sufficiently deep pocket coinciding with their active site to make this strategy feasible (1).Accounting for conformational heterogeneity could greatly expand the repertoire of available drug targets by revealing cryptic sites-transient pockets that are not readily visible in experimental structures, yet can inhibit enzymatic activity via allostery or block protein-protein interactions (2-5). For example, they may provide previously undescribed druggable pockets on proteins that are already considered viable targets, or even make it possible to target proteins that are currently considered undruggable. Targeting these sites may be easier than targeting the active site if there is no natural ligand to outcompete. Finally, crypt...

Detection of secondary binding sites in proteins using fragment screening

“…Furthermore, many computational methods have been developed to predict ligand-binding sites from protein sequence conservation, 3D structure (12), or both (13). These tools have had some success in predicting sites from the apo structure (14), including the presence of cryptic pockets (15). However, identifying ligand-induced pockets remains a largely unsolved problem.…”

Estimation and uncertainty of reversible Markov models

“…Examples include the folding of proteins into their native folded structure, 20,22,23 the dynamics of natively unstructured proteins, 24,25 and the binding of a ligand to a target protein. 21,[26][27][28][29][30] There are two key steps in the construction of a MSM. At first, a suitable discretization of the continuous conformation space has to be obtained.…”

Discovery of multiple hidden allosteric sites by combining Markov state models and experiments

“…For example, there are a variety of methods for understanding how information flows from one region of a protein to another (15)(16)(17)(18)(19), as well as for identifying potential binding pockets (20)(21)(22)(23)(24). More recent work has accounted for both of these ingredients (25,26). Although these methods are important developments, many are only applicable to small proteins operating on fast timescales.…”

“…It already has one known hidden allosteric site (34), so it is an excellent system for testing whether our approach can discriminate allosteric sites from nonallosteric sites. Finally, β-lactamase is a large enough protein that it is reasonable to ask if there are other hidden allosteric sites, especially given our recent computational prediction that such sites exist (26). Newly predicted allosteric sites that are similar in nature to the known site could be attractive drug targets given that we already know ligands that bind the known hidden allosteric site are capable of modulating β-lactamase's activity.…”

“…Potential allosteric sites are then identified by querying the Markov model for local fluctuations that form pockets that are surrounded by residues whose rotameric orientations are correlated with those of the active site. These correlations are a property of the ensemble of structures the protein can adopt and give insight into where perturbations, such as ligand binding, are likely to exert allosteric control over distant sites, such as the active site (26).…”