CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites. Issue 4 (22nd February 2016)

• Record Type: Journal Article
• Authors: Cimermancic, Peter; Weinkam, Patrick; Rettenmaier, T. Justin; Bichmann, Leon; Keedy, Daniel A.; Woldeyes, Rahel A.; Schneidman-Duhovny, Dina; Demerdash, Omar N.; Mitchell, Julie C.; Wells, James A.; Fraser, James S.; Sali, Andrej
• Is Part Of: Journal of molecular biology. Volume 428:Issue 4(2016:Feb. 15)
• Page Start: 709
• Page End: 719
• Publication Date: 2016-02-22
• Subjects: cryptic binding sites -- protein dynamics -- undruggable proteins -- machine learning; PTP1B protein tyrosine phosphatase 1B -- SVM support vector machine -- AUC area under the curve; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2016.01.029
• Languages: English
• ISSNs: 0022-2836

Abstract

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 athttp://salilab.org/cryptosite . Graphical Abstract: Highlights: Bona fide cryptic sites identified by comparison of apo and holo protein structures. Features distinguishing cryptic sites and binding pockets identified. Efficient and accurate prediction of cryptic sites developed. Cryptic sites predicted for all human proteins of known structure. The "druggable" human proteome may be larger than previously estimated.