In silico directed chemical probing of the adenosine receptor family.

One of the grand challenges in chemical biology is identifying a small-molecule modulator for each individual function of all human proteins. Instead of targeting one protein at a time, an efficient approach to address this challenge is to target entire protein families by taking advantage of the relatively high levels of chemical promiscuity observed within certain boundaries of sequence phylogeny. We recently developed a computational approach to identifying the potential protein targets of compounds based on their similarity to known bioactive molecules for almost 700 targets. Here, we describe the direct identification of novel antagonists for all four adenosine receptor subtypes by applying our virtual profiling approach to a unique synthesis-driven chemical collection composed of 482 biologically-orphan molecules. These results illustrate the potential role of in silico target profiling to guide efficiently screening campaigns directed to discover new chemical probes for all members of a protein family.

[1]  S. Ekins,et al.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling , 2007, British journal of pharmacology.

[2]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[3]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.

[4]  Bryan L Roth,et al.  Screening the receptorome to discover the molecular targets for plant-derived psychoactive compounds: a novel approach for CNS drug discovery. , 2004, Pharmacology & therapeutics.

[5]  Pál Pacher,et al.  Adenosine receptors: therapeutic aspects for inflammatory and immune diseases , 2006, Nature Reviews Drug Discovery.

[6]  Jordi Mestres,et al.  SHED: Shannon Entropy Descriptors from Topological Feature Distributions , 2006, J. Chem. Inf. Model..

[7]  Ram Samudrala,et al.  Novel paradigms for drug discovery: computational multitarget screening. , 2008, Trends in pharmacological sciences.

[8]  Thierry Langer,et al.  In silico Target Fishing for Rationalized Ligand Discovery Exemplified on Constituents of Ruta graveolens , 2008, Planta medica.

[9]  Scott Boyer,et al.  Ligand-Based Approach to In Silico Pharmacology: Nuclear Receptor Profiling , 2006, J. Chem. Inf. Model..

[10]  D. Bojanic,et al.  Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. , 2005, Drug discovery today.

[11]  Stuart L Schreiber,et al.  Small molecules: the missing link in the central dogma , 2005, Nature chemical biology.

[12]  K. Jacobson,et al.  Adenosine receptors as therapeutic targets , 2006, Nature Reviews Drug Discovery.

[13]  Bryan L Roth,et al.  Massively parallel screening of the receptorome. , 2008, Combinatorial chemistry & high throughput screening.

[14]  Didier Rognan,et al.  In silico-guided target identification of a scaffold-focused library: 1,3,5-triazepan-2,6-diones as novel phospholipase A2 inhibitors. , 2006, Journal of medicinal chemistry.

[15]  Martin Jones,et al.  IUPHAR-DB: the IUPHAR database of G protein-coupled receptors and ion channels , 2008, Nucleic Acids Res..

[16]  S. Gessi,et al.  Adenosine receptor antagonists: translating medicinal chemistry and pharmacology into clinical utility. , 2008, Chemical reviews.

[17]  J. Mestres,et al.  A ligand-based approach to mining the chemogenomic space of drugs. , 2008, Combinatorial chemistry & high throughput screening.

[18]  Peter G. Schultz,et al.  In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen , 2008, Proceedings of the National Academy of Sciences.

[19]  M. F. Proença,et al.  Synthesis of 9-hydroxyalkyl-substituted purines from the corresponding 4-(C-cyanoformimidoyl)imidazole-5-amines , 1992 .

[20]  Mindy I. Davis,et al.  A quantitative analysis of kinase inhibitor selectivity , 2008, Nature Biotechnology.

[21]  T. Insel,et al.  NIH Molecular Libraries Initiative , 2004, Science.

[22]  A. Bender,et al.  In silico target fishing: Predicting biological targets from chemical structure , 2006 .

[23]  C. Lipinski.  Lead- and drug-like compounds: the rule-of-five revolution. , 2004, Drug discovery today. Technologies.

[24]  Xin Wen,et al.  BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..

[25]  Dragos Horvath,et al.  Predicting ADME properties and side effects: the BioPrint approach. , 2003, Current opinion in drug discovery & development.

[26]  M. Zaki,et al.  The reactions of diaminomaleonitrile with isocyanates and either aldehydes or ketones revisited. , 2001, The Journal of organic chemistry.

[27]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..