Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Unexplored therapeutic opportunities in the human genome

A Corrigendum to this article was published on 23 March 2018

This article has been updated

Abstract

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Target development level categories applied to the human proteome.
Figure 2: Patterns of target development level distribution across different data: visualizing the knowledge deficit.

Change history

  • 23 March 2018

    In the version of this article that was originally published online, an older version of the data set categorizing proteins into target development levels was used to create Figure 1 than the version used to create Table 1, and data from Figure 1 were referred to at several points in the text of the article. Figure 1 and the associated text have been updated to match Table 1 in the online and print versions of the article. The authors apologize for any inconvenience that this error may have caused.

References

  1. Knowles, J. & Gromo, G. Target selection in drug discovery. Nat. Rev. Drug Discov. 2, 63–69 (2003).

    CAS  PubMed  Google Scholar 

  2. Edwards, A. M. et al. Too many roads not taken. Nature 470, 163–165 (2011).

    CAS  PubMed  Google Scholar 

  3. Alberts, B., Kirschner, M. W., Tilghman, S. & Varmus, H. Rescuing US biomedical research from its systemic flaws. Proc. Natl Acad. Sci. USA 111, 5773–5777 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Kim, S. et al. PubChem Substance and Compound databases. Nucleic Acids Res. 44, D1202–D1213 (2016).

    CAS  PubMed  Google Scholar 

  5. Gaulton, A. et al. The ChEMBL database in 2017. Nucleic Acids Res. 45, D945–D954 (2017).

    CAS  PubMed  Google Scholar 

  6. Tomczak, K., Czerwin´ska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19, A68–A77 (2015).

    Google Scholar 

  7. Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).

    PubMed  PubMed Central  Google Scholar 

  8. Nickerson, R. S. Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175–220 (1998).

    Google Scholar 

  9. Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017).

    CAS  PubMed  Google Scholar 

  10. Ursu, O. et al. DrugCentral: online drug compendium. Nucleic Acids Res. 45, D932–D939 (2017).

    CAS  PubMed  Google Scholar 

  11. Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009).

    CAS  PubMed  Google Scholar 

  12. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X. & Jensen, L. J. Diseases: text mining and data integration of disease-gene associations. Methods 74, 83–89 (2015).

    CAS  PubMed  Google Scholar 

  14. Kiermer, V. Antibodypedia. Nat. Methods 5, 860–861 (2008).

    CAS  Google Scholar 

  15. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).

  16. Papadatos, G. et al. SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic Acids Res. 44, D1220–1228 (2016).

    CAS  PubMed  Google Scholar 

  17. Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, baw100 (2016).

    PubMed  PubMed Central  Google Scholar 

  18. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).

    CAS  PubMed  Google Scholar 

  19. Hajduk, P. J., Huth, J. R. & Tse, C. Predicting protein druggability. Drug Discov. Today 10, 1675–1682 (2005).

    CAS  PubMed  Google Scholar 

  20. Hopkins, A. L. & Groom, C. R. The druggable genome. Nat. Rev. Drug Discov. 1, 727–730 (2002).

    CAS  PubMed  Google Scholar 

  21. Surade, S. & Blundell, T. L. Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem. Biol. 19, 42–50 (2012).

    CAS  PubMed  Google Scholar 

  22. Kubinyi, H. Drug research: myths, hype and reality. Nat. Rev. Drug Discov. 2, 665–668 (2003).

    CAS  PubMed  Google Scholar 

  23. Yang, X. et al. Widespread expansion of protein interaction capabilities by alternative splicing. Cell 164, 805–817 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Mestres, J., Gregori-Puigjané, E., Valverde, S. & Solé, R. V. Data completeness—the Achilles heel of drug-target networks. Nat. Biotechnol. 26, 983–984 (2008).

    CAS  PubMed  Google Scholar 

  25. Schreiber, S. L. et al. Advancing biological understanding and therapeutics discovery with small-molecule probes. Cell 161, 1252–1265 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Austin, C. P., Brady, L. S., Insel, T. R. & Collins, F. S. NIH molecular libraries initiative. Science 306, 1138–1139 (2004).

    CAS  PubMed  Google Scholar 

  27. Southan, C. et al. The IUPHAR/BPS guide to pharmacology in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands. Nucleic Acids Res. 44, D1054–D1068 (2016).

    CAS  PubMed  Google Scholar 

  28. Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug Discov. 14, 475–486 (2015).

    CAS  PubMed  Google Scholar 

  29. Hunter, S. et al. InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306–312 (2012).

    CAS  PubMed  Google Scholar 

  30. Kruger, F. A., Gaulton, A., Nowotka, M. & Overington, J. P. PPDMs-a resource for mapping small molecule bioactivities from ChEMBL to Pfam-A protein domains. Bioinformatics 31, 776–778 (2015).

    CAS  PubMed  Google Scholar 

  31. Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J. & Bork, P. Drug target identification using side-effect similarity. Science 321, 263–266 (2008).

    CAS  PubMed  Google Scholar 

  32. Keiser, M. J. et al. Predicting new molecular targets for known drugs. Nature 462, 175–181 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Huang, X. & Dixit, V. M. Drugging the undruggables: exploring the ubiquitin system for drug development. Cell Res. 26, 484–498 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lai, A. C. & Crews, C. M. Induced protein degradation: an emerging drug discovery paradigm. Nat. Rev. Drug Discov. 16, 101–114 (2017).

    CAS  PubMed  Google Scholar 

  35. Sakamoto, K. M. et al. Protacs: chimeric molecules that target proteins to the Skp1–Cullin–F box complex for ubiquitination and degradation. Proc. Natl Acad. Sci. 98, 8554–8559 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Gadd, M. S. et al. Structural basis of PROTAC cooperative recognition for selective protein degradation. Nat. Chem. Biol. 13, 514–521 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Mungall, C. J. et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res. 45, D712–D722 (2017).

    CAS  PubMed  Google Scholar 

  38. Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45, D896–D901 (2017).

    CAS  PubMed  Google Scholar 

  40. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  41. GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

  42. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    PubMed  Google Scholar 

  43. Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Lenat, D. B. & Feigenbaum, E. A. On the thresholds of knowledge. Artif. Intell. 47, 185–250 (1991).

    Google Scholar 

  45. Fishilevich, S. et al. Genic insights from integrated human proteomics in GeneCards. Database 2016, baw030 (2016).

    PubMed  PubMed Central  Google Scholar 

  46. Smirnov, D. A. et al. Genetic variation in radiation-induced cell death. Genome Res. 22, 332–339 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Garrison, J. L. & Knight, Z. A. Linking smell to metabolism and aging. Science 358, 718–719 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Kliewer, S. A., Lehmann, J. M. & Willson, T. M. Orphan nuclear receptors: shifting endocrinology into reverse. Science 284, 757–760 (1999).

    CAS  PubMed  Google Scholar 

  49. Willson, T. M., Jones, S. A., Moore, J. T. & Kliewer, S. A. Chemical genomics: functional analysis of orphan nuclear receptors in the regulation of bile acid metabolism. Med. Res. Rev. 21, 513–522 (2001).

    CAS  PubMed  Google Scholar 

  50. Moore, L. B. et al. Orphan nuclear receptors constitutive androstane receptor and pregnane X receptor share xenobiotic and steroid ligands. J. Biol. Chem. 275, 15122–15127 (2000).

    CAS  PubMed  Google Scholar 

  51. Pellicciari, R. et al. 6alpha-ethyl-chenodeoxycholic acid (6-ECDCA), a potent and selective FXR agonist endowed with anticholestatic activity. J. Med. Chem. 45, 3569–3572 (2002).

    CAS  PubMed  Google Scholar 

  52. Hambruch, E., Kinzel, O. & Kremoser, C. On the pharmacology of farnesoid X receptor agonists: give me an 'A', like in 'acid'. Nucl. Recep. Res. 3, 101207 (2016).

    Google Scholar 

  53. Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143–1151 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Roth, B. L., Sheffler, D. J. & Kroeze, W. K. Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nat. Rev. Drug Discov. 3, 353–359 (2004).

    CAS  PubMed  Google Scholar 

  56. Hernandez, P. A. et al. Mutations in the chemokine receptor gene CXCR4 are associated with WHIM syndrome, a combined immunodeficiency disease. Nat. Genet. 34, 70–74 (2003).

    CAS  PubMed  Google Scholar 

  57. Sternini, C. Receptors and transmission in the brain-gut axis: potential for novel therapies. III. Mu-opioid receptors in the enteric nervous system. Am. J. Physiol. Gastrointest. Liver Physiol. 281, G8–15 (2001).

    CAS  PubMed  Google Scholar 

  58. Sternini, C. Taste receptors in the gastrointestinal tract. IV. Functional implications of bitter taste receptors in gastrointestinal chemosensing. Am. J. Physiol. Gastrointest. Liver Physiol. 292, G457–461 (2007).

    PubMed  Google Scholar 

  59. Rockman, H. A., Koch, W. J. & Lefkowitz, R. J. Seven-transmembrane-spanning receptors and heart function. Nature 415, 206–212 (2002).

    CAS  PubMed  Google Scholar 

  60. Elphick, G. F. et al. The human polyomavirus, JCV, uses serotonin receptors to infect cells. Science 306, 1380–1383 (2004).

    CAS  PubMed  Google Scholar 

  61. Roth, B. L. & Kroeze, W. K. Integrated approaches for genome-wide interrogation of the druggable non-olfactory G protein-coupled receptor superfamily. J. Biol. Chem. 290, 19471–19477 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Elkins, J. M. et al. Comprehensive characterization of the Published Kinase Inhibitor Set. Nat. Biotechnol. 34, 95–103 (2016).

    CAS  PubMed  Google Scholar 

  63. Lin, X. et al. Life beyond kinases: structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors. J. Med. Chem. 55, 5749–5759 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Huang, X.-P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477–483 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Chan, J. D. et al. The anthelmintic praziquantel is a human serotoninergic G-protein-coupled receptor ligand. Nat. Commun. 8, 1910 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. Roth, B. L. Drugs and valvular heart disease. N. Engl. J. Med. 356, 6–9 (2007).

    CAS  PubMed  Google Scholar 

  67. Kroeze, W. K. et al. PRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome. Nat. Struct. Mol. Biol. 22, 362–369 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Lansu, K. et al. In silico design of novel probes for the atypical opioid receptor MRGPRX2. Nat. Chem. Biol. 13, 529–536 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Pafilis, E. et al. The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text. PLoS ONE 8, e65390 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Okajima, D., Kudo, G. & Yokota, H. Antidepressant-like behavior in brain-specific angiogenesis inhibitor 2-deficient mice. J. Physiol. Sci. 61, 47–54 (2011).

    PubMed  Google Scholar 

  71. Katsu, T. et al. The human frizzled-3 (FZD3) gene on chromosome 8p21, a receptor gene for Wnt ligands, is associated with the susceptibility to schizophrenia. Neurosci. Lett. 353, 53–56 (2003).

    CAS  PubMed  Google Scholar 

  72. Wei, J. & Hemmings, G. P. Lack of a genetic association between the frizzled-3 gene and schizophrenia in a British population. Neurosci. Lett. 366, 336–338 (2004).

    CAS  PubMed  Google Scholar 

  73. Jeong, S. H., Joo, E. J., Ahn, Y. M., Lee, K. Y. & Kim, Y. S. Investigation of genetic association between human Frizzled homolog 3 gene (FZD3) and schizophrenia: results in a Korean population and evidence from meta-analysis. Psychiatry Res. 143, 1–11 (2006).

    CAS  PubMed  Google Scholar 

  74. Wu, P., Nielsen, T. E. & Clausen, M. H. Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discov. Today 21, 5–10 (2016).

    CAS  PubMed  Google Scholar 

  75. Zawistowski, J. S. et al. Enhancer remodeling during adaptive bypass to MEK inhibition is attenuated by pharmacologic targeting of the P-TEFb complex. Cancer Discov. 7, 302–321 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Kullmann, D. M. The neuronal channelopathies. Brain 125, 1177–1195 (2002).

    PubMed  Google Scholar 

  77. Gloyn, A. L. et al. Large-scale association studies of variants in genes encoding the pancreatic beta-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568–572 (2003).

    CAS  PubMed  Google Scholar 

  78. Marbán, E. Cardiac channelopathies. Nature 415, 213–218 (2002).

    PubMed  Google Scholar 

  79. Berman, R. M. et al. Antidepressant effects of ketamine in depressed patients. Biol. Psychiatry 47, 351–354 (2000).

    CAS  PubMed  Google Scholar 

  80. Kirby, T. Ketamine for depression: the highs and lows. Lancet Psychiatry 2, 783–784 (2015).

    PubMed  Google Scholar 

  81. Zanos, P. et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533, 481–486 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Pedersen, S. F., Klausen, T. K. & Nilius, B. The identification of a volume-regulated anion channel: an amazing Odyssey. Acta Physiol. 213, 868–881 (2015).

    CAS  Google Scholar 

  83. Niemeyer, B. A. Changing calcium: CRAC channel (STIM and Orai) expression, splicing, and posttranslational modifiers. Am. J. Physiol. Cell Physiol. 310, C701–709 (2016).

    PubMed  Google Scholar 

  84. Dauner, K., Lissmann, J., Jeridi, S., Frings, S. & Möhrlen, F. Expression patterns of anoctamin 1 and anoctamin 2 chloride channels in the mammalian nose. Cell Tissue Res. 347, 327–341 (2012).

    CAS  PubMed  Google Scholar 

  85. Pandey, A. K., Lu, L., Wang, X., Homayouni, R. & Williams, R. W. Functionally enigmatic genes: a case study of the brain ignorome. PLoS ONE 9, e88889 (2014).

    PubMed  PubMed Central  Google Scholar 

  86. Pfeffer, C. & Olsen, B. R. Editorial: Journal of negative results in biomedicine. J. Negat. Results Biomed. 1, 2 (2002).

    PubMed  PubMed Central  Google Scholar 

  87. Groth, P., Gibson, A. & Velterop, J. The anatomy of a nanopublication. Inf. Serv. Use 30, 51–56 (2010).

    Google Scholar 

  88. Agarwal, P. & Searls, D. B. Can literature analysis identify innovation drivers in drug discovery? Nat. Rev. Drug Discov. 8, 865–878 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Nguyen, D.-T. et al. Pharos: Collating protein information to shed light on the druggable genome. Nucleic Acids Res. 45, D995–D1002 (2017).

    CAS  PubMed  Google Scholar 

  90. Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2017).

    PubMed Central  Google Scholar 

  91. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).

  92. Griffith, M. et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat. Genet. 49, 170–174 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Koscielny, G. et al. Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Res. 45, D985–D994 (2017).

    CAS  PubMed  Google Scholar 

  94. Lin, Y. et al. Drug target ontology to classify and integrate drug discovery data. J. Biomed. Semant. 8, 50 (2017).

    Google Scholar 

  95. Maggon, K. Best-selling human medicines 2002–2004. Drug Discov. Today 10, 739–742 (2005).

    PubMed  Google Scholar 

  96. Stebbins, S. The world's 15 top selling drugs. 24/7 Wall St. http://247wallst.com/special-report/2016/04/26/top-selling-drugs-in-the-world/, (2016).

  97. Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B. & Gloriam, D. E. Trends in GPCR drug discovery: new agents, targets and indications. Nat. Rev. Drug Discov. 16, 829–842 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Shih, H.-P., Zhang, X. & Aronov, A. M. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nat. Rev. Drug Discov. 17, 19–33 (2018).

    CAS  PubMed  Google Scholar 

  99. Tartaglia, L. A. et al. Identification and expression cloning of a leptin receptor, OB-R. Cell 83, 1263–1271 (1995).

    CAS  PubMed  Google Scholar 

  100. Xie, J. et al. Activating Smoothened mutations in sporadic basal-cell carcinoma. Nature 391, 90–92 (1998).

    CAS  PubMed  Google Scholar 

  101. Lee, M. J. et al. Sphingosine-1-phosphate as a ligand for the G protein-coupled receptor EDG-1. Science 279, 1552–1555 (1998).

    CAS  PubMed  Google Scholar 

  102. Sakurai, T. et al. Orexins and orexin receptors: a family of hypothalamic neuropeptides and G protein-coupled receptors that regulate feeding behavior. Cell 92, 573–585 (1998).

    CAS  PubMed  Google Scholar 

  103. Abifadel, M. et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nat. Genet. 34, 154–156 (2003).

    CAS  PubMed  Google Scholar 

  104. Kojima, M. et al. Ghrelin is a growth-hormone-releasing acylated peptide from stomach. Nature 402, 656–660 (1999).

    CAS  PubMed  Google Scholar 

  105. Temel, J. S. et al. Anamorelin in patients with non-small-cell lung cancer and cachexia (ROMANA 1 and ROMANA 2): results from two randomised, double-blind, phase 3 trials. Lancet Oncol. 17, 519–531 (2016).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by US National Institutes of Health (NIH) grants U54 CA189205 and U24 224370 (Illuminating the Druggable Genome Knowledge Management Center (IDG KMC)) at the University of New Mexico, Novo Nordisk Foundation Center for Protein Research, European Bioinformatics Institute (EBI) and University of Miami, U54 CA189201 and U24 CA224260 (A.M., Mount Sinai), P30 CA118100 (T.I.O., G.N.G. and L.A.S., UNM) and UL1 TR001449 (T.I.O. and L.A.S.), UM1 HG006370 (International Mouse Phenotyping Consortium, T.F.M. and I.T.), U01 MH104974 (B.L.R.), U01 MH104984 (S.T.), U01 MH105028 (M.T.M.), U01 MH105026 (J.Q. and A.M., Baylor) and U01 MH104999, R01 CA177993 and U24 DK116204 (S.G. and G.L.J.) and by the European Molecular Biology Laboratory (EMBL) and Wellcome Trust Strategic Awards WT086151/Z/08/Z and WT104104/Z/14/Z (A.G., A.H., A.R.L., A.K., J.P.O., and G.P.); and by Novo Nordisk Foundation Denmark grant NNF14CC0001 (S.B., L.J.L. and D.W). R.G., A.J., D.T.N., A.S., N.S., and G.Z.K. were supported by the Intramural Research Program, National Center for Advancing Translational Sciences (NCATS) and by U54 CA189205. Dedicated to Francisc Schneider (1933–2017).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tudor I. Oprea.

Ethics declarations

Competing interests

S.B. and L.J.J. are co-founders, shareholders and scientific advisory board members of Intomics A/S, an omics data integration company. A.C. and C.R. are employees of IQVIA, a company serving the combined industries of health information technologies and clinical research. I.T. is a current employee of Google Germany. G.P. is a current employee of GlaxoSmithKline, a global health-care company. D.M. is a current employee of AstraZeneca, a global, research-based biopharmaceutical company. J.P.O. is currently an employee of Medicines Discovery Catapult, a UK government-funded facility for collaborative research and development.

Related links

PowerPoint slides

Supplementary information

Supplementary Box S1

Context, time and knowledge management (PDF 190 kb)

Supplementary File S2

Plot of bioactivity values for major target classes. (PDF 675 kb)

Supplementary Information

Supplementary S3 table (XLSX 12754 kb)

Supplementary S4 table

Statistical significance results for the four validating metrics shown in Figure 2a (PDF 117 kb)

Supplementary S5 Box

Spotlight on the ionizing radiation proteome (PDF 301 kb)

Supplementary S7 table

Spotlight on selectivity (PDF 132 kb)

Supplementary information

Supplementary S8 table (XLSX 321 kb)

Supplementary information

Supplementary S9 table (XLSX 1011 kb)

Supplementary information

Supplementary S10 table (XLSX 15 kb)

Supplementary information

Supplementary S11 table (XLSX 111 kb)

Supplementary File S11

IMS Health (MIDAS) global drug sales data (2011-2015), organized by ATC level 2 codes and by protein class, normalized to percentage values. (PDF 1026 kb)

Glossary

Drug

Externally administered, possibly endogenous but mostly xenobiotic, substances that are administered to patients in order to influence the outcome of a disease, syndrome or condition.

Drug targets

Molecular entities present in living systems that, upon interaction with therapeutic agents or their by-products, result in modified biological responses that lead to therapeutic outcomes. The interaction between a drug and its target leads, directly or indirectly, to observable clinical outcomes.

Druggable genome

Originally defined by Hopkins and Groom as the set of genes that encode proteins that could be modulated by an orally administered small molecule, as estimated by Lipinski's 'rule of five' guidelines.

Mode of action

Referred to as 'mechanism of action' when the molecular interactions are well understood; describes the way in which drugs exert their intended therapeutic action, resulting in the intended therapeutic outcome.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Oprea, T., Bologa, C., Brunak, S. et al. Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 17, 317–332 (2018). https://doi.org/10.1038/nrd.2018.14

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd.2018.14

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing