CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Abstract

Many proteins have small molecule-binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery, and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets, but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially “druggable” human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite web server is available at http://salilab.org/cryptosite.

Targeted Protein Degradation by Small Molecules

“…the thermal shift assay) (102), NMR-based fragment screens (103), competitive binding screens when a weak ligand is available (69), and affinity-based selection are all popular techniques (104). Other techniques to identify cryptic binding sites may also identify novel pockets on protein surfaces that could be targeted by degradation technologies (105, 106). …”

Druggability Assessment in TRAPP using Machine Learning Approaches

“…To demonstrate how conformational changes impact druggability, we applied our druggability prediction models to two systems containing cryptic binding sites. [38][39][40] It has been reported that appropriately sized pockets of the biologically relevant drug targets are necessary for the strong binding of drug-sized ligands. 41,42 For the target proteins that are considered tractable but less-druggable, it is of interest to identify their cryptic sites and to exploit those sites to boost the druggability of the drug targets.…”

Cooperative changes in solvent exposure identify cryptic pockets, conformational switches, and allosteric coupling

“…Identifying these cryptic sites could offer new druggable sites on established drug targets, provide a means to inhibit proteins that are currently considered undruggable, or enable the enhancement of desirable activities. 3,4 Raltegravir, for example, is a first-line treatment for HIV infection 5 that inhibits HIV integrase by binding a cryptic pocket. However, the population of a given excited state is often too low to detect experimentally unless a binding partner that stabilizes it is present.…”