Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics.

A target is druggable if it can be modulated in vivo by a drug-like molecule. The general properties of oral drugs are summarized by the 'rule of 5' which specifies parameters related to size and lipophilicity. Structure-based target druggability assessment consists of predicting ligand-binding sites on the protein that are complementary to these drug-like properties. Automated identification of ligand-binding sites can use geometrical considerations alone or include specific physicochemical properties of the protein surface. Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets. The recent availability of several validation sets of druggable versus undruggable targets has helped fuel the development of more elaborate methods.

[1]  Yong Zhou,et al.  Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere , 2010, Bioinform..

[2]  M. Schroeder,et al.  LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation , 2006, BMC Structural Biology.

[3]  Ajay N. Jain,et al.  Automatic identification and representation of protein binding sites for molecular docking , 1997, Protein science : a publication of the Protein Society.

[4]  John Kuriyan,et al.  Crystal structures of the kinase domain of c-Abl in complex with the small molecule inhibitors PD173955 and imatinib (STI-571). , 2001, Cancer research.

[5]  E. Istvan,et al.  Statin inhibition of HMG-CoA reductase: a 3-dimensional view. , 2003, Atherosclerosis. Supplements.

[6]  R. Abagyan,et al.  Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes* , 2005, Molecular & Cellular Proteomics.

[7]  X. Barril,et al.  Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. , 2010, Journal of medicinal chemistry.

[8]  D. Levitt,et al.  POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. , 1992, Journal of molecular graphics.

[9]  P. Hajduk,et al.  Druggability indices for protein targets derived from NMR-based screening data. , 2005, Journal of medicinal chemistry.

[10]  Hongbo Zhu,et al.  MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets , 2011, Bioinform..

[11]  C. Venkatachalam,et al.  LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. , 2003, Journal of molecular graphics & modelling.

[12]  Alexander D. MacKerell,et al.  Binding Response: A Descriptor for Selecting Ligand Binding Site on Protein Surfaces , 2007, J. Chem. Inf. Model..

[13]  Scott P. Brown,et al.  Effects of Conformational Dynamics on Predicted Protein Druggability , 2006, ChemMedChem.

[14]  Frank K. Pettit,et al.  HotPatch: a statistical approach to finding biologically relevant features on protein surfaces. , 2007, Journal of molecular biology.

[15]  P. Goodford A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[16]  Thomas A. Halgren,et al.  Identifying and Characterizing Binding Sites and Assessing Druggability , 2009, J. Chem. Inf. Model..

[17]  P E Bourne,et al.  The Protein Data Bank. , 2002, Nucleic acids research.

[18]  R. Stevens,et al.  High-Resolution Crystal Structure of an Engineered Human β2-Adrenergic G Protein–Coupled Receptor , 2007, Science.

[19]  J. Delaney Finding and filling protein cavities using cellular logic operations. , 1992, Journal of molecular graphics.

[20]  Nagasuma Chandra,et al.  PocketDepth: a new depth based algorithm for identification of ligand binding sites in proteins. , 2008, Journal of structural biology.

[21]  B. Honig,et al.  On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites , 2006, Proteins.

[22]  Pieter F. W. Stouten,et al.  Fast prediction and visualization of protein binding pockets with PASS , 2000, J. Comput. Aided Mol. Des..

[23]  Haruki Nakamura,et al.  Prediction of ligand‐binding sites of proteins by molecular docking calculation for a random ligand library , 2011, Protein science : a publication of the Protein Society.

[24]  M Hendlich,et al.  LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. , 1997, Journal of molecular graphics & modelling.

[25]  D. Ringe,et al.  Locating and characterizing binding sites on proteins , 1996, Nature Biotechnology.

[26]  F. J. Luque,et al.  Binding site detection and druggability index from first principles. , 2009, Journal of medicinal chemistry.

[27]  D. Goodsell,et al.  Automated prediction of ligand‐binding sites in proteins , 2007, Proteins.

[28]  S. J. Campbell,et al.  Visualizing the drug target landscape. , 2010, Drug discovery today.

[29]  Sandor Vajda,et al.  Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. , 2007, Journal of medicinal chemistry.

[30]  Richard M. Jackson,et al.  Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites , 2005, Bioinform..

[31]  R. Abagyan,et al.  Comprehensive identification of "druggable" protein ligand binding sites. , 2004, Genome informatics. International Conference on Genome Informatics.

[32]  Daniel R. Caffrey,et al.  Structure-based maximal affinity model predicts small-molecule druggability , 2007, Nature Biotechnology.

[33]  Philip E. Bourne,et al.  A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites , 2007, BMC Bioinformatics.

[34]  R. Stevens,et al.  The 2.6 Angstrom Crystal Structure of a Human A2A Adenosine Receptor Bound to an Antagonist , 2008, Science.

[35]  R. Laskowski SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. , 1995, Journal of molecular graphics.

[36]  G. Schneider,et al.  PocketPicker: analysis of ligand binding-sites with shape descriptors , 2007, Chemistry Central Journal.

[37]  C. Frömmel,et al.  The automatic search for ligand binding sites in proteins of known three-dimensional structure using only geometric criteria. , 1996, Journal of molecular biology.

[38]  Robert P. Sheridan,et al.  Drug-like Density: A Method of Quantifying the "Bindability" of a Protein Target Based on a Very Large Set of Pockets and Drug-like Ligands from the Protein Data Bank , 2010, J. Chem. Inf. Model..

[39]  Sandor Vajda,et al.  Computational mapping identifies the binding sites of organic solvents on proteins , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[40]  H. Edelsbrunner,et al.  Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design , 1998, Protein science : a publication of the Protein Society.

[41]  M. Jacobson,et al.  Binding-Site Assessment by Virtual Fragment Screening , 2010, PloS one.

[42]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[43]  John P. Overington,et al.  Genomic-scale prioritization of drug targets: the TDR Targets database , 2008, Nature Reviews Drug Discovery.

[44]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[45]  David S. Wishart,et al.  HMDB: a knowledgebase for the human metabolome , 2008, Nucleic Acids Res..

[46]  G. Kellogg,et al.  A novel and efficient tool for locating and characterizing protein cavities and binding sites , 2010, Proteins.

[47]  Chris M. W. Ho,et al.  Cavity search: An algorithm for the isolation and display of cavity-like binding regions , 1990, J. Comput. Aided Mol. Des..

[48]  Matthias Rarey,et al.  Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets , 2010, J. Chem. Inf. Model..

[49]  Ryan G. Coleman,et al.  Protein Pockets: Inventory, Shape, and Comparison , 2010, J. Chem. Inf. Model..

[50]  Vincent Le Guilloux,et al.  Fpocket: An open source platform for ligand pocket detection , 2009, BMC Bioinformatics.

[51]  John P. Overington,et al.  How many drug targets are there? , 2006, Nature Reviews Drug Discovery.

[52]  G J Kleywegt,et al.  Detection, delineation, measurement and display of cavities in macromolecular structures. , 1994, Acta crystallographica. Section D, Biological crystallography.