Skip to main content

Structure-Based Computational Approaches for Small-Molecule Modulation of Protein-Protein Interactions

  • Protocol
Protein-Protein Interactions

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1278))

Abstract

Three-dimensional structures of proteins offer an opportunity for the rational design of small molecules to modulate protein-protein interactions. The presence of a well-defined binding pocket on the surface of protein complexes, particularly at their interface, can be used for docking-based virtual screening of chemical libraries. Several approaches have been developed to identify binding pockets that are implemented in programs such as SiteMap, fpocket, and FTSite. These programs enable the scoring of these pockets to determine whether they are suitable to accommodate high-affinity small molecules. Virtual screening of commercial or combinatorial libraries can be carried out to enrich these libraries and select compounds for further experimental validation. In virtual screening, a compound library is docked to the target protein. The resulting structures are scored and ranked for the selection and experimental validation of top candidates. Molecular docking has been implemented in a number of computer programs such as AutoDock Vina. We select a set of protein-protein interactions that have been successfully inhibited with small molecules in the past. Several computer programs are applied to identify pockets on the surface, and molecular docking is conducted in an attempt to reproduce the binding pose of the inhibitors. The results highlight the strengths and limitations of computational methods for the design of PPI inhibitors.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144:986–998

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Ngounou Wetie AG, Sokolowska I, Woods AG et al (2013) Protein–protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches. Cell Mol Life Sci 71:205–228

    Google Scholar 

  3. White AW, Westwell AD, Brahemi G (2008) Protein–protein interactions as targets for small-molecule therapeutics in cancer. Expert Rev Mol Med 10:e8

    Article  PubMed  Google Scholar 

  4. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Lipman DJ, Pearson WR (1985) Rapid and sensitive protein similarity searches. Science 227:1435–1441

    Article  CAS  PubMed  Google Scholar 

  6. Li L, Bum-Erdene K, Baenziger PH et al (2010) BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome. Nucleic Acids Res 38:D765–D773

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Huang YJ, Hang D, Lu LJ et al (2008) Targeting the human cancer pathway protein interaction network by structural genomics. Mol Cell Proteomics 7(10):2048–2060

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  9. Li L, Meroueh SO (2008) Receptor-ligand interactions in biological systems. In: Encyclopedia for the life sciences. Wiley, London, p. 19. http://onlinelibrary.wiley.com/book/10.1002/9780470048672/homepage/EditorsContributors.html

  10. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69:146–148

    Article  CAS  PubMed  Google Scholar 

  11. Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168

    Article  PubMed Central  PubMed  Google Scholar 

  12. Kuhn D, Weskamp N, Hullermeier E et al (2007) Functional classification of protein kinase binding sites using cavbase. ChemMedChem 2:1432–1447

    Article  CAS  PubMed  Google Scholar 

  13. Ngan CH, Hall DR, Zerbe B et al (2012) FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics 28:286–287

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389

    Article  CAS  PubMed  Google Scholar 

  15. Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53:5858–5867

    Article  CAS  PubMed  Google Scholar 

  16. Stark C, Breitkreutz BJ, Reguly T et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Licata L, Briganti L, Peluso D et al (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. UniProt C (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40:D71–D75

    Article  Google Scholar 

  19. Porter CT, Bartlett GJ, Thornton JM (2004) The catalytic site atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res 32:D129–D133

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Leach AR, Gillet VJ, Lewis RA et al (2009) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53:539–558

    Article  Google Scholar 

  21. Hubbard RE (2011) Structure-based drug discovery and protein targets in the CNS. Neuropharmacology 60:7–23

    Article  CAS  PubMed  Google Scholar 

  22. Cheng T, Li Q, Zhou Z et al (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14:133–141

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  23. Khanna M, Wang F, Jo I et al (2011) Targeting multiple conformations leads to small molecule inhibitors of the uPAR·uPA protein–protein interaction that block cancer cell invasion. ACS Chem Biol 6:1232–1243

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Scheper J, Guerra-Rebollo M, Sanclimens G et al (2010) Protein–protein interaction antagonists as novel inhibitors of non-canonical polyubiquitylation. PLoS One 5:e11403

    Article  PubMed Central  PubMed  Google Scholar 

  25. Koes D, Khoury K, Huang Y et al (2012) Enabling large-scale design, synthesis and validation of small molecule protein–protein antagonists. PLoS One 7:e32839

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Geppert T, Bauer S, Hiss JA et al (2012) Immunosuppressive small molecule discovered by structure-based virtual screening for inhibitors of protein–protein interactions. Angew Chem Int Edit 51:258–261

    Article  CAS  Google Scholar 

  27. Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    Article  CAS  PubMed  Google Scholar 

  28. Makino S, Kuntz ID (1997) Automated flexible ligand docking method and its application for database search. J Comput Chem 18:1812–1825

    Article  CAS  Google Scholar 

  29. Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202

    Article  CAS  PubMed  Google Scholar 

  30. Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    Article  CAS  PubMed  Google Scholar 

  31. Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 245:43–53

    Article  CAS  PubMed  Google Scholar 

  32. Rarey M, Kramer B, Lengauer T et al (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    Article  CAS  PubMed  Google Scholar 

  33. Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  PubMed  Google Scholar 

  34. Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759

    Article  CAS  PubMed  Google Scholar 

  35. Pierce BG, Hourai Y, Weng Z (2011) Accelerating protein docking in ZDOCK using an advanced 3D convolution library. PLoS One 6:e24657

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. McGann M (2011) FRED pose prediction and virtual screening accuracy. J Chem Inf Model 51:578–596

    Article  CAS  PubMed  Google Scholar 

  37. Pedretti A, Villa L, Vistoli G (2004) VEGA – an open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. J Comput-Aided Mol Des 18:167–173

    Article  CAS  PubMed  Google Scholar 

  38. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321

    Article  CAS  PubMed  Google Scholar 

  39. Abagyan R, Totrov M, Kuznetsov D (1994) ICM – a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comp Chem 15:488–506

    Article  CAS  Google Scholar 

  40. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    PubMed Central  CAS  PubMed  Google Scholar 

  41. Obiol-Pardo C, Alcarraz-Vizán G, Cascante M et al (2012) Diphenyl urea derivatives as inhibitors of transketolase: a structure-based virtual screening. PLoS One 7:e32276

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Dessal AL, Prades R, Giralt E et al (2011) Rational design of a selective covalent modifier of G protein βγ subunits. Mol Pharm 79:24–33

    Article  CAS  Google Scholar 

  43. Trosset J-Y, Dalvit C, Knapp S et al (2006) Inhibition of protein–protein interactions: the discovery of druglike β-catenin inhibitors by combining virtual and biophysical screening. Proteins 64:60–67

    Article  CAS  PubMed  Google Scholar 

  44. Grüneberg S, Stubbs MT, Klebe G (2002) Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation. J Med Chem 45:3588–3602

    Article  PubMed  Google Scholar 

  45. Elokely KM, Doerksen RJ (2013) Docking Challenge: Protein Sampling and Molecular Docking Performance. J Chem Inf Model 53:1934–1945

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Lill MA, Winiger F, Vedani A et al (2005) Impact of Induced Fit on Ligand Binding to the Androgen Receptor: A Multidimensional QSAR Study To Predict Endocrine-Disrupting Effects of Environmental Chemicals. J Med Chem 48:5666–5674

    Article  CAS  PubMed  Google Scholar 

  47. Sherman W, Day T, Jacobson MP et al (2005) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49:534–553

    Article  Google Scholar 

  48. Arooj M, Sakkiah S, Kim S et al (2013) A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors. PLoS One 8:e63030

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Zhou S, Li Y, Hou T (2013) Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. J Chem Inf Model 53:982–996

    Article  CAS  PubMed  Google Scholar 

  50. Li Y, Kim DJ, Ma W et al (2011) Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. J Chem Inf Model 51:2904–2914

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  51. Isvoran A, Badel A, Craescu C et al (2011) Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein–protein interactions. BMC Struct Biol 11:24

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Knegtel RMA, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266:424–440

    Article  CAS  PubMed  Google Scholar 

  53. Carlson HA, Masukawa KM, Rubins K et al (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 43:2100–2114

    Article  CAS  PubMed  Google Scholar 

  54. Kukol A (2011) Consensus virtual screening approaches to predict protein ligands. Eur J Med Chem 46:4661–4664

    Article  CAS  PubMed  Google Scholar 

  55. Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Greenwood JR, Calkins D, Sullivan AP et al (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aid Mol Des 24:591–604

    Article  CAS  Google Scholar 

  57. Cramer CJ, Truhlar DG (1992) An SCF solvation model for the hydrophobic effect and absolute free energies of aqueous solvation. Science 256:213–217

    Article  CAS  PubMed  Google Scholar 

  58. Cramer CJ, Truhlar DG (1992) AM1-SM2 and PM3-SM3 parameterized SCF solvation models for free energies in aqueous solution. J Comput Aided Mol Des 6:629–666

    Article  CAS  PubMed  Google Scholar 

  59. Hawkins PCD, Skillman AG, Nicholls A (2006) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50:74–82

    Article  Google Scholar 

  60. Tetko IV, Gasteiger J, Todeschini R et al (2005) Virtual computational chemistry laboratory-design and description. J Comput-Aided Mol Des 19:453–463

    Article  CAS  PubMed  Google Scholar 

  61. Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  62. Li Q, Cheng T, Wang Y et al (2010) PubChem as a public resource for drug discovery. Drug Discov Today 15:1052–1057

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  63. Knox C, Law V, Jewison T et al (2011) DrugBank 3.0: a comprehensive resource for “omics” research on drugs. Nucleic Acids Res 39:D1035–D1041

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  64. Liu T, Lin Y, Wen X et al (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–D201

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  65. Still WC, Tempczyk A, Hawley RC et al (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112:6127–6129

    Article  CAS  Google Scholar 

  66. Luo R, David L, Gilson MK (2002) Accelerated Poisson–Boltzmann calculations for static and dynamic systems. J Comput Chem 23:1244–1253

    Article  CAS  PubMed  Google Scholar 

  67. Eldridge MD, Murray CW, Auton TR et al (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445

    Article  CAS  PubMed  Google Scholar 

  68. Li L, Wang B, Meroueh SO (2011) Support vector regression scoring of receptor–ligand complexes for rank-ordering and virtual screening of chemical libraries. J Chem Inf Model 51:2132–2138

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  69. Muegge I, Martin YC (1999) A general and fast scoring function for protein − ligand interactions: a simplified potential approach. J Med Chem 42:791–804

    Article  CAS  PubMed  Google Scholar 

  70. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein–ligand interactions. J Mol Biol 295:337–356

    Article  CAS  PubMed  Google Scholar 

  71. Li L, Khanna M, Jo I et al (2011) Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J Chem Inf Model 51:755–759

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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

    Google Scholar 

  73. Malek TR (2003) The main function of IL-2 is to promote the development of T regulatory cells. J Leukoc Biol 74:961–965

    Article  CAS  PubMed  Google Scholar 

  74. Willis S, Day CL, Hinds MG et al (2003) The Bcl-2-regulated apoptotic pathway. J Cell Sci 116:4053–4056

    Article  CAS  PubMed  Google Scholar 

  75. Moll UM, Petrenko O (2003) The MDM2-p53 interaction. Mol Cancer Res 1:1001–1008

    CAS  PubMed  Google Scholar 

  76. Muller M, Demeret C (2012) The HPV E2-host protein–protein interactions: a complex hijacking of the cellular network. Open Virol J 6:173–189

    Article  PubMed Central  PubMed  Google Scholar 

  77. Hughes FJ, Romanos MA (1993) E1 protein of human papillomavirus is a DNA helicase/ATPase. Nucleic Acids Res 21:5817–5823

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  78. Pazos M, Natale P, Vicente M (2013) A specific role for the ZipA protein in cell division: stabilization of the FtsZ protein. J Biol Chem 288:3219–3226

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  79. Locksley RM, Killeen N, Lenardo MJ (2001) The TNF and TNF receptor superfamilies: integrating mammalian biology. Cell 104:487–501

    Article  CAS  PubMed  Google Scholar 

Download references

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samy O. Meroueh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Xu, D., Wang, B., Meroueh, S.O. (2015). Structure-Based Computational Approaches for Small-Molecule Modulation of Protein-Protein Interactions. In: Meyerkord, C., Fu, H. (eds) Protein-Protein Interactions. Methods in Molecular Biology, vol 1278. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2425-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2425-7_5

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2424-0

  • Online ISBN: 978-1-4939-2425-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics