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Review
. 2018 Jun:44:1-8.
doi: 10.1016/j.cbpa.2018.05.003. Epub 2018 May 23.

Cryptic binding sites on proteins: definition, detection, and druggability

Sandor Vajda  1 , Dmitri Beglov  2 , Amanda E Wakefield  3 , Megan Egbert  2 , Adrian Whitty  4
Affiliations
Review

Cryptic binding sites on proteins: definition, detection, and druggability

Sandor Vajda et al. Curr Opin Chem Biol. 2018 Jun.

Abstract

Many proteins in their unbound structures lack surface pockets appropriately sized for drug binding. Hence, a variety of experimental and computational tools have been developed for the identification of cryptic sites that are not evident in the unbound protein but form upon ligand binding, and can provide tractable drug target sites. The goal of this review is to discuss the definition, detection, and druggability of such sites, and their potential value for drug discovery. Novel methods based on molecular dynamics simulations are particularly promising and yield a large number of transient pockets, but it has been shown that only a minority of such sites are generally capable of binding ligands with substantial affinity. Based on recent studies, current methodology can be improved by combining molecular dynamics with fragment docking and machine learning approaches.

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Figures

Figure 1
Figure 1
Types of cryptic sites. In all cases the bound structure is colored orange, the bound ligand is shown as yellow sticks, and the unbound structure superimposed over the bound one is colored cyan. PDB codes are shown in parenthesis. (a) Unbound (1W50) and ligand-bound (3IXJ) structures of beta-secretase 1 (BACE-1) protease, the latter co-crystallized with a isophthalamide inhibitor. In the unbound structure the binding pocket is too open, and the loop is closing down on the ligand upon binding. (b) Unbound (2GFC) and ligand-bound (2JDS) structures of cAMP-dependent protein kinase, the latter co-crystallized with the ATP-competitive inhibitor A-443654. In the unbound structure the activation loop protrudes into the binding site and would clash with the inhibitor superimposed from the bound structure. (c) Distributions of druggability scores in the unliganded structures of BACE-1 protease (homologs of 1W50) and cAMP-dependent protein kinase (homologs of 2GFC). Based on the druggability score, in the BACE-1 protease the pockets are almost evenly distributed between conformations resembling the unbound and bound forms. In contrast, in the cAMP-dependent protein kinase unbound-like conformations dominate. (d) Unbound (1JWP) and ligand-bound (1PZO) structures of TEM β-lactamase. The bound structure 1PZO includes two small inhibitor molecules bound between helices 11 and 12.
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References

    1. Cimermancic P., Weinkam P., Rettenmaier T.J., Bichmann L., Keedy D.A., Woldeyes R.A., Schneidman-Duhovny D., Demerdash O.N., Mitchell J.C., Wells J.A. CryptoSite: expanding the druggable proteome by characterization and prediction of cryptic binding sites. J Mol Biol. 2016;428:709–719. - PMC - PubMed

    2. A benchmark set of 93 unbound–bound protein pairs is created in which each unbound structure has a site considered cryptic due to its low pocket score, and each bound structure has a functionally relevant ligand bound at the site. The cryptic sites are comprehensively characterized in terms of their sequence, structure, and dynamics attributes. Relying on this characterization, machine learning was used to predict cryptic sites with relatively high accuracy. The resulting CryptoSite method was employed to predict cryptic sites in the entire structurally characterized human proteome, increasing the size of the potentially ‘druggable’ human proteome from ∼40% to ∼78% of disease-associated proteins. The CryptoSite Web server is available at http://salilab.org/cryptosite.

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    2. The geometry of a cryptic site was used to identify an inhibitor and two activators of TEM β-lactamase. To identify hits, a library of compounds was first virtually screened against either the crystal structure of the known cryptic pocket or an ensemble of structures obtained by Markov State Models based on molecular dynamics simulations. The hit compounds were screened experimentally and were shown to have modest effects on TEM activity. The approach is proposed for targeting proteins whose crystal structures lack obvious druggable pockets, and for identifying both inhibitory and activating small-molecule modulators.

    1. Kimura S.R., Hu H.P., Ruvinsky A.M., Sherman W., Favia A.D. Deciphering cryptic binding sites on proteins by mixed-solvent molecular dynamics. J Chem Inf Model. 2017;57:1388–1401. - PubMed
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    2. The nature and dynamic properties of cryptic sites were investigated by performing molecular dynamics simulations on three pharmacologically relevant targets, including TEM β-lactamase. It was found that if the computations were started with the proteins in the open (i.e. bound) conformation, but without the ligands present, the pockets promptly closed. However, adding fragment sized molecules as probes occasionally caused the re-opening of cryptic sites. The observed mechanism of cryptic site formation is suggestive of an interplay between induced-fit and conformational selection. Employing this insight, a novel Hamiltonian Replica Exchange-based method Sampling Water Interfaces through Scaled Hamiltonians (SWISH) was developed for the identification of cryptic sites.

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