Identifying compound efficacy targets in phenotypic drug discovery

Drug Discovery Today

Volumn 21, Issue 1, 2016, Pages 82-89

  Schirle, Markus ^a   Jenkins, Jeremy L ^a  

^a NOVARTIS INSTITUTES FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EPISTATIC PROTEIN; PROTEIN; UNCLASSIFIED DRUG; DRUG;

CHROMATIN ASSEMBLY AND DISASSEMBLY; COVALENT BOND; DRUG ACTIVITY; DRUG DEVELOPMENT; DRUG EFFICACY; DRUG TARGETING; ELECTROPHILICITY; EPISTASIS; FUNCTIONAL GENOMICS; GENE DELETION; GENE REPRESSION; GENETIC SCREENING; HAPLOINSUFFICIENCY; HIGH THROUGHPUT SCREENING; HUMAN; INDUCED RESISTANCE; NEXT GENERATION SEQUENCING; PHENOTYPE; PROTEIN BINDING; PROTEIN DOMAIN; PROTEIN PROTEIN INTERACTION; PROTEOMICS; REVIEW; CHEMISTRY; METABOLISM; PROCEDURES;

DRUG DISCOVERY; HUMANS; PHARMACEUTICAL PREPARATIONS; PHENOTYPE; PROTEINS;

EID: 84960199094     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2015.08.001     Document Type: Review

References (68)

1. D.C. Swinney, and J. Anthony How were new medicines discovered? Nat. Rev. Drug Discov. 10 2011 507 519
2. J.A. Lee, and E.L. Berg Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches J. Biomol. Screen. 18 2013 1143 1155
3. U. Rix, and G. Superti-Furga Target profiling of small molecules by chemical proteomics Nat. Chem. Biol. 5 2009 616 624
4. N. Kanoh, and et al. Photo-cross-linked small-molecule affinity matrix for facilitating forward and reverse chemical genetics Angew. Chem. Int. Ed. Engl. 44 2005 3559 3562
5. M. Bantscheff, and et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors Nat. Biotechnol. 25 2007 1035 1044
6. S.E. Ong, and et al. Identifying the proteins to which small-molecule probes and drugs bind in cells Proc. Natl. Acad. Sci. U. S. A. 106 2009 4617 4622
7. Y. Oda, and et al. Quantitative chemical proteomics for identifying candidate drug targets Anal. Chem. 75 2003 2159 2165
8. M. Bantscheff, and et al. Quantitative mass spectrometry in proteomics: a critical review Anal. Bioanal. Chem. 389 2007 1017 1031
9. M. Bantscheff, and et al. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present Anal. Bioanal. Chem. 404 2012 939 965
10. M. Bantscheff, and et al. Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes Nat. Biotechnol. 29 2011 255 265
11. X. Cai, and et al. PIKfyve, a class III PI kinase, is the target of the small molecular IL-12/IL-23 inhibitor apilimod and a player in Toll-like receptor signaling Chem. Biol. 20 2013 912 921
12. F. Bassilana, and et al. Target identification for a Hedgehog pathway inhibitor reveals the receptor GPR39 Nat. Chem. Biol. 10 2014 343 349
13. Y. Akbulut, and et al. (-)-Englerin A is a potent and selective activator of TRPC4 and TRPC5 calcium channels Angew. Chem. Int. Ed. Engl. 54 2015 3787 3791
14. A.L. MacKinnon, and et al. Photo-leucine incorporation reveals the target of a cyclodepsipeptide inhibitor of cotranslational translocation J. Am. Chem. Soc. 129 2007 14560 14561
15. C. Drahl, and et al. Protein-reactive natural products Angew. Chem. Int. Ed. Engl. 44 2005 5788 5809
16. E. Weerapana, and et al. Disparate proteome reactivity profiles of carbon electrophiles Nat. Chem. Biol. 4 2008 405 407
17. M.J. Evans, and et al. Target discovery in small-molecule cell-based screens by in situ proteome reactivity profiling Nat. Biotechnol. 23 2005 1303 1307
18. A.E. Speers, and et al. Activity-based protein profiling in vivo using a copper(i)-catalyzed azide-alkyne [3+2] cycloaddition J. Am. Chem. Soc. 125 2003 4686 4687
19. C.M. Salisbury, and B.F. Cravatt Activity-based probes for proteomic profiling of histone deacetylase complexes Proc. Natl. Acad. Sci. U. S. A. 104 2007 1171 1176
20. B. Lomenick, and et al. Target identification using drug affinity responsive target stability (DARTS) Proc. Natl. Acad. Sci. U. S. A. 106 2009 21984 21989
21. P.F. Liu, and et al. Energetics-based discovery of protein-ligand interactions on a proteomic scale J. Mol. Biol. 408 2011 147 162
22. G.M. West, and et al. Quantitative proteomics approach for identifying protein-drug interactions in complex mixtures using protein stability measurements Proc. Natl. Acad. Sci. U. S. A. 107 2010 9078 9082
23. M.M. Savitski, and et al. Tracking cancer drugs in living cells by thermal profiling of the proteome Science 346 2014 1255784
24. E.J. Licitra, and J.O. Liu A three-hybrid system for detecting small ligand-protein receptor interactions Proc. Natl. Acad. Sci. U. S. A. 93 1996 12817 12821
25. A.R. Shepard, and et al. Identification of PDE6D as a molecular target of anecortave acetate via a methotrexate-anchored yeast three-hybrid screen ACS Chem. Biol. 8 2013 549 558
26. M. Caligiuri, and et al. MASPIT: three-hybrid trap for quantitative proteome fingerprinting of small molecule-protein interactions in mammalian cells Chem. Biol. 13 2006 711 722
27. J. Huang, and et al. Finding new components of the target of rapamycin (TOR) signaling network through chemical genetics and proteome chips Proc. Natl. Acad. Sci. U. S. A. 101 2004 16594 16599
28. M. Salcius, and et al. SEC-TID: A label-free method for small-molecule target identification J. Biomol. Screen. 19 2014 917 927
29. J. Zhu, and et al. Protein interaction discovery using parallel analysis of translated ORFs (PLATO) Nat. Biotechnol. 31 2013 331 334
30. G. Giaever, and C. Nislow The yeast deletion collection: a decade of functional genomics Genetics 197 2014 451 465
31. D. Hoepfner, and et al. High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions Microbiol. Res. 169 2014 107 120
32. A.Y. Lee, and et al. Mapping the cellular response to small molecules using chemogenomic fitness signatures Science 344 2014 208 211
33. A.C. Palmer, and R. Kishony Opposing effects of target overexpression reveal drug mechanisms Nat. Commun. 5 2014 4296
34. S.A. Wacker, and et al. Using transcriptome sequencing to identify mechanisms of drug action and resistance Nat. Chem. Biol. 8 2012 235 237
35. C. Kasap, and et al. DrugTargetSeqR: a genomics- and CRISPR-Cas9-based method to analyze drug targets Nat. Chem. Biol. 10 2014 626 628
36. J.H. Reiling, and et al. A haploid genetic screen identifies the major facilitator domain containing 2A (MFSD2A) transporter as a key mediator in the response to tunicamycin Proc. Natl. Acad. Sci. U. S. A. 108 2011 11756 11765
37. Z. Huang, and et al. A functional variomics tool for discovering drug-resistance genes and drug targets Cell Rep. 3 2013 577 585
38. A. Bill, and et al. Variomics screen identifies the re-entrant loop of the calcium-activated chloride channel ANO1 that facilitates channel activation J. Biol. Chem. 290 2015 889 903
39. A. Arnoldo, and et al. A genome scale overexpression screen to reveal drug activity in human cells Genome Med. 6 2014 32
40. R. Palchaudhuri, and P.J. Hergenrother Transcript profiling and RNA interference as tools to identify small molecule mechanisms and therapeutic potential ACS Chem. Biol. 6 2011 21 33
41. C.J. Matheny, and et al. Next-generation NAMPT inhibitors identified by sequential high-throughput phenotypic chemical and functional genomic screens Chem. Biol. 20 2013 1352 1363
42. J. Zhou, and et al. A kinome screen identifies checkpoint kinase 1 (CHK1) as a sensitizer for RRM1-dependent gemcitabine efficacy PLOS ONE 8 2013 e58091
43. J. Zhou, and et al. Modulation of the ribonucleotide reductase-antimetabolite drug interaction in cancer cell lines J. Nucleic Acids 2010 2010 597098
44. Y. Smurnyy, and et al. DNA sequencing and CRISPR-Cas9 gene editing for target validation in mammalian cells Nat. Chem. Biol. 10 2014 623 625
45. T.R. Hughes, and et al. Functional discovery via a compendium of expression profiles Cell 102 2000 109 126
46. J. Lamb, and et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease Science 313 2006 1929 1935
47. K. Lai, and et al. Integrated compound profiling screens identify the mitochondrial electron transport chain as the molecular target of the natural products manassantin, sesquicillin, and arctigenin ACS Chem. Biol. 8 2013 257 267
48. F.J. King, and et al. Pathway reporter assays reveal small molecule mechanisms of action J. Assoc. Lab. Autom. 14 2009 374 382
49. M. Kitagawa, and et al. Metabolomic identification of the target of the filopodia protrusion inhibitor glucopiericidin A Chem. Biol. 17 2010 989 998
50. J. Barretina, and et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity Nature 483 2012 603 607
51. J.L. Jenkins, and et al. Bridging chemical and biological data: implications for pharmaceutical drug discovery Comput. Approaches Cheminform. Bioinform. 2012 10.1002/9781118131411.ch2
52. C.A. Lipinski, and et al. Parallel worlds of public and commercial bioactive chemistry data J. Med. Chem. 58 2015 2068 2076
53. G. Papadatos, and J.P. Overington The ChEMBL database: a taster for medicinal chemists Future Med. Chem. 6 2014 361 364
54. J. Ratnam, and et al. The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research PLOS ONE 9 2014 e115460
55. J.L. Jenkins Large-scale QSAR in target prediction and phenotypic HTS assessment Mol. Inform. 31 2012 508 514
56. Y. Zhou, and et al. Large-scale annotation of small-molecule libraries using public databases J. Chem. Inf. Model. 47 2007 1386 1394
57. M. Schenone, and et al. Target identification and mechanism of action in chemical biology and drug discovery Nat. Chem. Biol. 9 2013 232 240
58. A.M. Wassermann, and et al. A screening pattern recognition method finds new and divergent targets for drugs and natural products ACS Chem. Biol. 9 2014 1622 1631
59. D. Plouffe, and et al. In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen Proc. Natl. Acad. Sci. U. S. A. 105 2008 9059 9064
60. M.J. Keiser, and et al. Relating protein pharmacology by ligand chemistry Nat. Biotechnol. 25 2007 197 206
61. G.M. Nidhi, and et al. Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases J. Chem. Inf. Model. 46 2006 1124 1133
62. B. Ramsundar, and et al. Massively multitask networks for drug discovery 2015 Cornell University Library Available at: http://arxiv.org/abs/1502.02072
63. F. Martínez-Jiménez, and et al. Target prediction for an open access set of compounds active against Mycobacterium tuberculosis PLoS Comput. Biol. 9 2013 e1003253
64. B. Chen, and et al. Assessing drug target association using semantic linked data PLoS Comput. Biol. 8 2012 e1002574
65. A. Bender, and et al. Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data J. Proteome Res. 8 2009 2575 2585
66. C.R. Andersson, and et al. Quantitative chemogenomics: machine-learning models of protein-ligand interaction Curr. Top. Med. Chem. 11 2011 1978 1993
67. I. Kufareva, and et al. Compound activity prediction using models of binding pockets or ligand properties in 3D Curr. Top. Med. Chem. 12 2012 1869 1882
68. S. Jaeger, and et al. Causal network models for predicting compound targets and driving pathways in cancer J. Biomol. Screen. 19 2014 791 802