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J Mol Biol. 2016 Feb 22;428(4):709-19. doi: 10.1016/j.jmb.2016.01.029. Epub 2016 Feb 5.

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

Author information

  • 1Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Graduate Group in Biological and Medical Informatics,University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: peter.cimermancic@ucsf.edu.
  • 2Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA.
  • 3Graduate Group in Chemistry and Chemical Biology, University of California, San Francisco, San Francisco, CA 94158, USA; Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
  • 4Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Graduate Group in Chemistry and Chemical Biology, University of California, San Francisco, San Francisco, CA 94158, USA.
  • 5Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.
  • 6Departments of Biochemistry and Mathematics, University of Wisconsin-Madison, Madison, WI 53706, USA.
  • 7Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Cellular and Molecular Pharmacology and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
  • 8Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Pharmaceutical Chemistry and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA. Electronic address: http://salilab.org.

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.

KEYWORDS:

cryptic binding sites; machine learning; protein dynamics; undruggable proteins

PMID:
26854760
PMCID:
PMC4794384
DOI:
10.1016/j.jmb.2016.01.029
[PubMed - indexed for MEDLINE]
Free PMC Article
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