Computational analysis of ligand relationships within target families

Current Opinion in Chemical Biology

Volumn 12, Issue 3, 2008, Pages 352-358

  Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DOPAMINE 2 RECEPTOR BLOCKING AGENT; G PROTEIN COUPLED RECEPTOR; LIGAND; SEROTONIN 2A ANTAGONIST;

BAYES THEOREM; BIOASSAY; CHEMICAL GENETICS; COMPUTER ANALYSIS; DRUG TARGETING; MATHEMATICAL COMPUTING; MOLECULAR INTERACTION; PREDICTION; PROCESS OPTIMIZATION; REVIEW; STRUCTURE ACTIVITY RELATION;

COMPUTATIONAL BIOLOGY; DRUG DESIGN; LIGANDS; PROTEIN BINDING; SUBSTRATE SPECIFICITY;

EID: 43949145741     PISSN: 13675931     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cbpa.2008.01.044     Document Type: Review

References (38)

1. Hopkins A.L., and Groom C.R. The druggable genome. Nat Rev Drug Discov 1 (2002) 727-730
2. Overington J.P., Al-Lazikani B., and Hopkins A.L. How many drug targets are there?. Nat Rev Drug Discov 5 (2006) 993-996
3. Bredel M., and Jacoby E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 5 (2004) 262-275
4. Rognan D. Chemogenomic approaches to rational drug design. Br J Pharmacol 152 (2007) 38-42. An informative review covering many computational approaches at both the ligand and target level with relevance to chemogenomics.
5. Stockwell B.R. Exploring biology with small organic molecules. Nature 432 (2004) 846-854
6. Spring D.R. Chemical genetics to chemical genomics: small molecules offer big insights. Chem Soc Rev 34 (2005) 472-482
7. Jorgensen W.L. The many roles of computation in drug discovery. Science 303 (2004) 1813-1818
8. Fliri A.F., Loging W.T., Thadeio P.F., and Volkmann R.A. Biological spectra analysis: linking biological activity profiles to molecular structure. Proc Natl Acad Sci U S A 102 (2005) 261-266
9. Fliri A.F., Loging W.T., Thadeio P.F., and Volkmann R.A. Biospectra analysis: model proteome characterizations for linking molecular structure and biological response. J Med Chem 48 (2005) 6918-6925
10. Kauvar L.M., Higgins D.L., Villar H.O., Sportsman J.R., Engquist-Goldstein A., Bukar R., Bauer K.E., Dilley H., and Rocke D.M. Predicting ligand binding to proteins by affinity fingerprinting. Chem Biol 2 (1995) 107-118
11. Fabian M.A., Biggs III W.H., Treiber D.K., Atteridge C.E., Azimioara M.D., Benedetti M.G., Carter T.A., Ciceri P., Edeen P.T., Floyd M., et al. A small molecule-kinase interaction map for clinical kinase inhibitors. Nat Biotechnol 23 (2005) 329-336. Profiling of an array of kinase inhibitors including drugs and clinical candidates against a significant portion of the kinome. The study shows that many inhibitors engage in interactions with multiple kinase targets.
12. Mestres J., Martin-Couce L., Gregori-Puigjane E., Cases M., and Boyer S. Ligand-based approaches to in silico pharmacology: nuclear receptor profiling. J Chem Inf Model 46 (2006) 2725-2736
13. Nidhi, Glick M., Davies J.W., and Jenkins J.L. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 46 (2006) 1124-1133
14. Steindl T.M., Schuster D., Laggner C., and Langer T. Parallel screening: a novel concept in pharmacophore modeling and virtual screening. J Chem Inf Model 46 (2006) 2146-2157
15. Deng Z., Chuaqui C., and Singh J. Structural interaction fingerprint (SIFt): a novel method for analyzing protein-ligand interactions. J Med Chem 47 (2004) 337-344
16. Chuaqui C., Deng Z., and Singh J. Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening. J Med Chem 48 (2005) 121-133
17. Paolini G.V., Shapland R.H.B., van Hoorn W.P., Mason J.S., and Hopkins A.L. Global mapping of pharmacological space. Nat Biotechnol 24 (2006) 805-815. Systematic analysis of drug-target interactions leads to an integration of drug and target space and provides an instructive view of polypharmacology. Hundreds of Bayesian classifiers were generated to predict drug targets.
18. Keiser M.J., Roth B.L., Armbuster B.N., Ernberger P., Irwin J.J., and Shoichet B.K. Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25 (2007) 197-206. Chemical similarity of active compounds was used to organize target space revealing biologically meaningful target clusters and potential secondary targets for a number of drugs.
19. Yildirim M.A., Goh K.-I., Cusick M.E., Barabási A.-L., and Vidal M. Drug-target network. Nat Biotechnol 25 (2007) 1119-1126. Drug-target interactions are mapped onto a protein-protein interaction network combined with disease associations and gene expression information, thus enabling quantitative network analysis. Among other findings, the authors showed that about 40% of targets are associated with multiple diseases and that drugs directed against novel targets act discontinuously in the network structure (and are therefore termed 'jumping drugs').
20. Cleves A.N., and Jain A.N. Robust ligand-based modeling of the biological targets of known drugs. J Med Chem 49 (2006) 2921-2938
21. Pirard B., and Matter H. Matrix metalloproteinase target family landscape: a chemometrical approach to ligand selectivity based on protein binding site analysis. J Med Chem 49 (2006) 51-69
22. Muller P., Lena G., Boilard E., Bezzine S., Lambeau G., Guichard G., and Rognan D. In silico-guided target identification of a scaffold-focused library: 1,3,5-triazepan-2,6-diones as novel phospholipase A2 inhibitors. J Med Chem 49 (2006) 6768-6778
23. Hermann J.C., Marti-Arbona R., Fedorov A.A., Fedorov E., Almo S.C., Shoichet B.K., and Raushel F.M. Structure-based activity prediction for an enzyme of unknown function. Nature 448 (2007) 775-779. This study and [24] illustrate for the first time that docking of potential substrates into the active site of an enzyme with unknown function can reveal its activity. Here a library of high-energy intermediates of candidate substrates was docked and top-scoring candidates suggested a functional assignment of a target enzyme from structural genomics.
24. Song L., Kalyanaraman C., Fedorov A.A., Fedorov E.V., Glasner M.E., Brown S., Imker H.J., Babbitt P.C., Almo S.C., Jacobson M.P., and Gerlt J.A. Prediction and assignment of function for a divergent N-succinyl amino acid racemase. Nat Chem Biol 3 (2007) 486-491
25. Bock J.R., and Gough D.A. Virtual screens for ligands of orphan G protein-coupled receptors. J Chem Inf Model 45 (2005) 1402-1414. Generation of combined ligand-receptor descriptor vectors for SVM classification and identification of 'true' ligand-GPCR complexes.
26. Oloff S., Zhang S., Sukumar N., Breneman C., and Tropsha A. Chemometric analysis of ligand receptor complementarity: identifying complementary ligands based on receptor information (CoLiBRI). J Chem Inf Model 46 (2006) 844-851
27. Frye S.V. Structure-activity relationship homology (SARAH): a conceptual framework for drug discovery in the genomics era. Chem Biol 6 (1999) R3-R7
28. Schuffenhauer A., Floersheim P., Acklin P., and Jacoby E. Similarity metrics for ligands reflecting the similarity of target proteins. J Chem Inf Comput Sci 43 (2003) 391-405
29. Xia X., Maliski E.G., Gallant P., and Rogers D. Classification of kinase inhibitors using a Bayesian model. J Med Chem 47 (2004) 4463-4470
30. Stumpfe D., Ahmed H.E.A., Vogt I., and Bajorath J. Methods for computer-aided chemical biology. Part 1. Design of a benchmark system for the evaluation of compound selectivity. Chem Biol Drug Des 70 (2007) 182-194
31. Vogt I., Stumpfe D., Ahmed H.E.A., and Bajorath J. Methods for computer-aided chemical biology. Part 2. Evaluation of compound selectivity using 2D molecular fingerprints. Chem Biol Drug Des 70 (2007) 195-2005. First application of fingerprints in 'selectivity searching' for ligands of closely related GPCR and protease targets on the basis of especially designed compound selectivity sets reported in [30].
32. Schreiber S.L. Target-oriented and diversity-oriented synthesis in drug discovery. Science 287 (2000) 1964-1969
33. Tan D.S. Diversity-oriented synthesis: exploring the intersections between chemistry and biology. Nat Chem Biol 1 (2005) 74-84
34. Peltason L., and Bajorath J. Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem Biol 14 (2007) 489-497. SAR analysis through systematic comparison of two-dimensional and three-dimensional similarity and relative potential differences of multiple ligands taken from series of complex crystal structures of selected target enzymes. The analysis confirmed the presence of heterogeneous SARs that are prime targets for virtual compound screening and chemical optimization.
35. Peltason L., and Bajorath J. SAR Index: quantifying the nature of structure-activity relationships. J Med Chem 50 (2007) 5571-5578. Derivation of a function to quantitatively describe SAR features given a set of active ligands and their potencies. On the basis of SAR Index calculations, SARs are assigned to major categories: continuous, discontinuous, heterogeneous-constrained, and heterogeneous-relaxed.
36. Shanmugasundaram V., Maggiora G.M., and Lajiness M.S. Hit-directed nearest-neighbor searching. J Med Chem 48 (2005) 240-248
37. Xue L., Godden J.W., Stahura F.L., and Bajorath J. A dual-fingerprint metric for the design of focused compound libraries and analogs. J Mol Model 7 (2001) 125-131
38. Root D.E., Flaherty S.P., Kelley B.P., and Stockwell B.R. Biological mechanism profiling using an annotated compound library. Chem Biol 10 (2003) 881-892