Skip to main content

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

  • Protocol
  • First Online:
Proteomics for Drug Discovery

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1647))

Abstract

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  1. Shoemaker BA, Panchenko AR, Bryant SH (2006) Finding biologically relevant protein domain interactions: conserved binding mode analysis. Protein Sci 15(2):352–361

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Aloy P, Russell RB (2004) Ten thousand interactions for the molecular biologist. Nat Biotechnol 22(10):1317–1321

    Article  CAS  PubMed  Google Scholar 

  3. Rolland T, Tasan M, Charloteaux B et al (2014) A proteome-scale map of the human interactome network. Cell 159(5):1212–1226

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yates CM, Sternberg MJ (2013) The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions. J Mol Biol 425(21):3949–3963

    Article  CAS  PubMed  Google Scholar 

  5. Teng S, Madej T, Panchenko A, Alexov E (2009) Modeling effects of human single nucleotide polymorphisms on protein-protein interactions. Biophys J 96(6):2178–2188

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li M, Goncearenco A, Panchenko AR (2017) Annotating mutational effects on proteins and protein interactions: designing novel and revisiting existing protocols. Methods Mol Biol 1550:235–260

    Article  PubMed  PubMed Central  Google Scholar 

  7. Filippakopoulos P, Qi J, Picaud S et al (2010) Selective inhibition of BET bromodomains. Nature 468(7327):1067–1073

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Smith BJ, Lee EF, Checco JW et al (2013) Structure-guided rational design of alpha/beta-peptide foldamers with high affinity for BCL-2 family prosurvival proteins. Chembiochem 14(13):1564–1572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhao Y, Aguilar A, Bernard D, Wang S (2015) Small-molecule inhibitors of the MDM2-p53 protein-protein interaction (MDM2 Inhibitors) in clinical trials for cancer treatment. J Med Chem 58(3):1038–1052

    Article  CAS  PubMed  Google Scholar 

  10. Haase HS, Peterson-Kaufman KJ, Lan Levengood SK et al (2012) Extending foldamer design beyond alpha-helix mimicry: alpha/beta-peptide inhibitors of vascular endothelial growth factor signaling. J Am Chem Soc 134(18):7652–7655

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Basse MJ, Betzi S, Morelli X, Roche P (2016) 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions. Database (Oxford) 2016

    Google Scholar 

  12. Aloy P, Ceulemans H, Stark A, Russell RB (2003) The relationship between sequence and interaction divergence in proteins. J Mol Biol 332(5):989–998

    Article  CAS  PubMed  Google Scholar 

  13. Ma B, Elkayam T, Wolfson H, Nussinov R (2003) Protein-protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proc Natl Acad Sci U S A 100(10):5772–5777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Valdar WS, Thornton JM (2001) Protein-protein interfaces: analysis of amino acid conservation in homodimers. Proteins 42(1):108–124

    Article  CAS  PubMed  Google Scholar 

  15. Guharoy M, Chakrabarti P (2005) Conservation and relative importance of residues across protein-protein interfaces. Proc Natl Acad Sci U S A 102(43):15447–15452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Goncearenco A, Shaytan AK, Shoemaker BA, Panchenko AR (2015) Structural perspectives on the evolutionary expansion of unique protein-protein binding sites. Biophys J 109(6):1295–1306

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Goncearenco A, Shoemaker BA, Zhang D et al (2014) Coverage of protein domain families with structural protein-protein interactions: current progress and future trends. Prog Biophys Mol Biol 116(2–3):187–193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Petrey D, Honig B (2014) Structural bioinformatics of the interactome. Annu Rev Biophys 43:193–210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mosca R, Ceol A, Aloy P (2013) Interactome3D: adding structural details to protein networks. Nat Methods 10(1):47–53

    Article  CAS  PubMed  Google Scholar 

  20. Tyagi M, Hashimoto K, Shoemaker BA et al (2012) Large-scale mapping of human protein interactome using structural complexes. EMBO Rep 13(3):266–271

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shoemaker BA, Zhang D, Thangudu RR et al (2010) Inferred Biomolecular Interaction Server—a web server to analyze and predict protein interacting partners and binding sites. Nucleic Acids Res 38(Database issue):D518–D524

    Article  CAS  PubMed  Google Scholar 

  22. Shoemaker BA, Zhang D, Tyagi M et al (2012) IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins. Nucleic Acids Res 40(Database issue):D834–D840

    Article  CAS  PubMed  Google Scholar 

  23. Kim S, Thiessen PA, Bolton EE et al (2016) PubChem Substance and Compound databases. Nucleic Acids Res 44(D1):D1202–D1213

    Article  CAS  PubMed  Google Scholar 

  24. Wang Y, Bryant SH, Cheng T (2017) PubChem BioAssay: 2017 update. Nucleic Acids Res 45(D1):D955–D963

    Article  CAS  PubMed  Google Scholar 

  25. Li M, Simonetti FL, Goncearenco A, Panchenko AR (2016) MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions. Nucleic Acids Res 44(W1):W494–W501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li M, Petukh M, Alexov E, Panchenko AR (2014) Predicting the impact of missense mutations on protein? Protein binding affinity. J Chem Theor Comput 10(4):1770–1780

    Article  CAS  Google Scholar 

  27. Yang W, Soares J, Greninger P (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41(Database issue):D955–D961

    CAS  PubMed  Google Scholar 

  28. Gilson MK, Liu T, Baitaluk M, Nicola G et al (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44(D1):D1045–D1053

    Article  CAS  PubMed  Google Scholar 

  29. Petukh M, Dai L, Alexov E (2016) SAAMBE: webserver to predict the charge of binding free energy caused by amino acids mutations. Int J Mol Sci 17(4):547

    Article  PubMed  PubMed Central  Google Scholar 

  30. Cukuroglu E, Gursoy A, Keskin O (2012) HotRegion: a database of predicted hot spot clusters. Nucleic Acids Res 40(Database issue):D829–D833

    Article  CAS  PubMed  Google Scholar 

  31. Estrada-Ortiz N, Neochoritis CG, Domling A (2016) How to design a successful p53-MDM2/X interaction inhibitor: a thorough overview based on crystal structures. ChemMedChem 11(8):757–772

    Article  CAS  PubMed  Google Scholar 

  32. Shangary S, Wang S (2009) Small-molecule inhibitors of the MDM2-p53 protein-protein interaction to reactivate p53 function: a novel approach for cancer therapy. Annu Rev Pharmacol Toxicol 49:223–241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Cinatl J, Speidel D, Hardcastle I, Michaelis M (2014) Resistance acquisition to MDM2 inhibitors. Biochem Soc Trans 42(4):752–757

    Article  CAS  PubMed  Google Scholar 

  34. Thangudu RR, Bryant SH, Panchenko AR, Madej T (2012) Modulating protein-protein interactions with small molecules: the importance of binding hotspots. J Mol Biol 415(2):443–453

    Article  CAS  PubMed  Google Scholar 

  35. Davis FP, Sali A (2010) The overlap of small molecule and protein binding sites within families of protein structures. PLoS Comput Biol 6(2):e1000668

    Article  PubMed  PubMed Central  Google Scholar 

  36. Marchler-Bauer A, Derbyshire MK, Gonzales NR et al (2015) CDD: NCBI’s conserved domain database. Nucleic Acids Res 43(D1):D222–D226

    Article  CAS  PubMed  Google Scholar 

  37. Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450(7172):1001–1009

    Article  CAS  PubMed  Google Scholar 

  38. Bogan AA, Thorn KS (1998) Anatomy of hot spots in protein interfaces. J Mol Biol 280(1):1–9

    Article  CAS  PubMed  Google Scholar 

  39. Graves B, Thompson T, Xia M, Janson C (2012) Activation of the p53 pathway by small-molecule-induced MDM2 and MDMX dimerization. Proc Natl Acad Sci U S A 109(29):11788–11793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ribeiro CJ, Rodrigues CM, Moreira R, Santos MM (2016) Chemical variations on the p53 reactivation theme. Pharmaceuticals (Basel) 9(2)

    Google Scholar 

  41. Perez-Sanchez H, Rezaei V, Mezhuyev V et al (2016) Developing science gateways for drug discovery in a grid environment. Spring 5(1):1300

    Article  Google Scholar 

  42. Beisken S, Meinl T, Wiswedel B et al (2013) KNIME-CDK: workflow-driven cheminformatics. BMC Bioinformatics 14:257

    Article  PubMed  PubMed Central  Google Scholar 

  43. Truszkowski A, Jayaseelan KV, Neumann S et al (2011) New developments on the cheminformatics open workflow environment CDK-Taverna. J Cheminform 3:54

    Article  PubMed  PubMed Central  Google Scholar 

  44. Mazanetz MP, Marmon RJ, Reisser CB, Morao I (2012) Drug discovery applications for KNIME: an open source data mining platform. Curr Top Med Chem 12(18):1965–1979

    Article  CAS  PubMed  Google Scholar 

  45. Nicola G, Berthold MR, Hedrick MP, Gilson MK (2015) Connecting proteins with drug-like compounds: open source drug discovery workflows with BindingDB and KNIME. Database (Oxford) 2015

    Google Scholar 

  46. Wolstencroft K, Haines R, Fellows D et al (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Res 41(Web Server issue):W557–W561

    Article  PubMed  PubMed Central  Google Scholar 

  47. Goble CA, Bhagat J, Aleksejevs S et al (2010) myExperiment: a repository and social network for the sharing of bioinformatics workflows. Nucleic Acids Res 38(Web Server issue):W677–W682

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bhagat J, Tanoh F, Nzuobontane E et al (2010) BioCatalogue: a universal catalogue of web services for the life sciences. Nucleic Acids Res 38(Web Server):W689–W694

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by the Intramural Research Program of the National Library of Medicine. We thank Sunghwan Kim for helpful discussions about PubChem.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna R. Panchenko .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Goncearenco, A., Li, M., Simonetti, F.L., Shoemaker, B.A., Panchenko, A.R. (2017). Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows. In: Lazar, I., Kontoyianni, M., Lazar, A. (eds) Proteomics for Drug Discovery. Methods in Molecular Biology, vol 1647. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7201-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7201-2_15

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7200-5

  • Online ISBN: 978-1-4939-7201-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics