Skip to main content
SpringerLink
Log in

Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions

  • Protocol
  • First Online:
Book cover Modeling Peptide-Protein Interactions

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1561))

  • 3024 Accesses

Abstract

Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html. Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
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

References

  1. Rual JF et al (2005) Towards a proteome-scale map of the human protein–protein interaction network. Nature 437:1173–1178

    Article  CAS  PubMed  Google Scholar 

  2. Giot L et al (2003) A protein interaction map of Drosophila melanogaster. Science 302:1727–1736

    Article  CAS  PubMed  Google Scholar 

  3. Uetz P et al (2000) A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403:623–627

    Article  CAS  PubMed  Google Scholar 

  4. Ito T et al (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci U S A 98:4569–4574

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Li S et al (2004) A map of the interactome network of the metazoan C. elegans. Science 303:540–543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fuxreiter M, Tompa P, Simon I (2007) Local structural disorder imparts plasticity on linear motifs. Bioinformatics 23:950–956

    Article  CAS  PubMed  Google Scholar 

  7. Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6:197–208

    Article  CAS  PubMed  Google Scholar 

  8. Dunker AK et al (2000) Intrinsic protein disorder in complete genomes. Genome Inf 11:161–171

    CAS  Google Scholar 

  9. Pawson T, Nash P (2003) Assembly of cell regulatory systems through protein interaction domains. Science 300:445–452

    Article  CAS  PubMed  Google Scholar 

  10. Petsalaki E, Russell R (2008) Peptide-mediated interactions in biological systems: new discoveries and applications. Curr Opin Biotechnol 19:344–350

    Article  CAS  PubMed  Google Scholar 

  11. Vagner J, Qu H, Hruby VJ (2008) Peptidomimetics, a synthetic tool of drug discovery. Curr Opin Chem Biol 12:292–296

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Neduva V, Russell RB (2006) Peptides mediating interaction networks: new leads at last. Curr Opin Biotechnol 17:465–471

    Article  CAS  PubMed  Google Scholar 

  13. Vanhee P et al (2011) Computational design of peptide ligands. Trends Biotechnol 29:231–239

    Article  CAS  PubMed  Google Scholar 

  14. London N, Raveh B, Schueler-Furman O (2013) Druggable protein-protein interactions – from hot spots to hot segments. Curr Opin Chem Biol 17:952–959

    Article  CAS  PubMed  Google Scholar 

  15. Rubinstein M, Niv MY (2009) Peptidic modulators of protein-protein interactions: progress and challenges in computational design. Biopolymers 91:505–513

    Article  CAS  PubMed  Google Scholar 

  16. Dundas J et al (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34:W116–W118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Petsalaki E et al (2009) Accurate prediction of peptide binding sites on protein surfaces. PLoS Comput Biol 5, e1000335

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ben-Shimon A, Eisenstein M (2010) Computational mapping of anchoring spots on protein surfaces. J Mol Biol 402:259–277

    Article  CAS  PubMed  Google Scholar 

  19. Dagliyan O et al (2011) Structural and dynamic determinants of protein–peptide recognition. Structure 19:1837–1845

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Trabuco LG et al (2012) PepSite: prediction of peptide-binding sites from protein surfaces. Nucleic Acids Res 40:W423–W427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lavi A et al (2013) Detection of peptide-binding sites on protein surfaces: the first step toward the modeling and targeting of peptide-mediated interactions. Proteins Struct Funct Bioinf 81:2096–2105

    Article  CAS  Google Scholar 

  22. Verschueren E et al (2013) Protein–peptide complex prediction through fragment interaction patterns. Structure 21:789–797

    Article  CAS  PubMed  Google Scholar 

  23. Saladin A et al (2014) PEP-SiteFinder: a tool for the blind identification of peptide binding sites on protein surfaces. Nucleic Acids Res 42:W221–W226

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yan C, Zou X (2015) Predicting peptide binding sites on protein surfaces by clustering chemical interactions. J Comp Chem 36:49–61

    Article  CAS  Google Scholar 

  25. London N, Raveh B, Schueler-Furman O (2013) Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Curr Opin Struct Biol 23:894–902

    Article  CAS  PubMed  Google Scholar 

  26. Tubert-Brohman I et al (2013) Improved docking of polypeptides with Glide. J Chem Inf Model 53:1689–1699

    Article  CAS  PubMed  Google Scholar 

  27. Rosenfeld R et al (1995) Flexible docking of peptides to class I major histocompatibility-complex receptors. Genet Anal Biomol Eng 12:1–21

    Article  CAS  Google Scholar 

  28. Bordner AJ, Abagyan R (2006) Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes. Proteins Struct Funct Bioinf 63:512–526

    Article  CAS  Google Scholar 

  29. Yanover C, Bradley P (2011) Large-scale characterization of peptide-MHC binding landscapes with structural simulations. Proc Natl Acad Sci U S A 108:6981–6986

    Article  PubMed  PubMed Central  Google Scholar 

  30. Antes I, Siu SWI, Lengauer T (2006) DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations. Bioinformatics 22:e16–e24

    Article  CAS  PubMed  Google Scholar 

  31. Niv MY, Weinstein H (2005) A flexible docking procedure for the exploration of peptide binding selectivity to known structures and homology models of PDZ domains. J Am Chem Soc 127:14072–14079

    Article  CAS  PubMed  Google Scholar 

  32. Staneva I, Wallin S (2009) All-atom Monte Carlo approach to protein–peptide binding. J Mol Biol 393:1118–1128

    Article  CAS  PubMed  Google Scholar 

  33. London N et al (2011) Identification of a novel class of farnesylation targets by structure-based modeling of binding specificity. PLoS Comput Biol 7, e1002170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Raveh B, London N, Schueler-Furman O (2010) Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins Struct Funct Bioinf 78:2029–2040

    CAS  Google Scholar 

  35. Raveh B et al (2011) Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One 6, e18934

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Trellet M, Melquiond AS, Bonvin AM (2013) A unified conformational selection and induced fit approach to protein–peptide docking. PLoS One 8, e58769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Antes I (2010) DynaDock: a new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins Struct Funct Bioinf 78:1084–1104

    Article  CAS  Google Scholar 

  38. Donsky E, Wolfson HJ (2011) PepCrawler: a fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors. Bioinformatics 27:2836–2842

    Article  CAS  PubMed  Google Scholar 

  39. Luitz MP, Zacharias M (2014) Protein-ligand docking using Hamiltonian replica exchange simulations with soft core potentials. J Chem Inf Model 54:1669–1675

    Article  CAS  PubMed  Google Scholar 

  40. Kurcinski M et al (2015) CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res 43:W419–W424

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ben-Shimon A, Niv MY (2015) AnchorDock: blind and flexible anchor-driven peptide docking. Structure 23:929–940

    Article  CAS  PubMed  Google Scholar 

  42. Lee H et al (2015) GalaxyPepDock: a protein–peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res 43:W431–W435

    Article  PubMed  PubMed Central  Google Scholar 

  43. Schindler CEM, de Vries SJ, Zacharias M (2015) Fully blind peptide-protein docking with pepATTRACT. Structure 23:1507–1515

    Article  CAS  PubMed  Google Scholar 

  44. Rentzsch R, Renard BY (2015) Docking small peptides remains a great challenge: an assessment using AutoDock Vina. Brief Bioinf 16(6):1045–1056

    Article  Google Scholar 

  45. Zacharias M (2003) Protein–protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci 12:1271–1282

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. May A, Zacharias M (2005) Accounting for global protein deformability during protein–protein and protein–ligand docking. Biochim Biophys Acta Proteins Proteomics 1754:225–231

    Article  CAS  Google Scholar 

  47. de Vries SJ et al (2015) A web interface for easy flexible protein–protein docking with ATTRACT. Biophys J 108:462–465

    Article  PubMed  PubMed Central  Google Scholar 

  48. Setny P, Bahadur R, Zacharias M (2012) Protein-DNA docking with a coarse-grained force field. BMC Bioinf 13:228

    Article  CAS  Google Scholar 

  49. Setny P, Zacharias M (2011) A coarse-grained force field for protein-RNA docking. Nucleic Acids Res 39:9118–9129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. May A, Zacharias M (2008) Protein-ligand docking accounting for receptor side chain and global flexibility in normal modes: evaluation on kinase inhibitor cross docking. J Med Chem 51:3499–3506

    Article  CAS  PubMed  Google Scholar 

  51. May A, Zacharias M (2007) Protein–protein docking in CAPRI using ATTRACT to account for global and local flexibility. Proteins Struct Funct Bioinf 69:774–780

    Article  CAS  Google Scholar 

  52. de Vries S, Zacharias M (2013) Flexible docking and refinement with a coarse-grained protein model using ATTRACT. Proteins Struct Funct Bioinf 81:2167–2174

    Article  CAS  Google Scholar 

  53. Lensink MF, Wodak SJ (2013) Docking, scoring, and affinity prediction in CAPRI. Proteins Struct Funct Bioinf 81:2082–2095

    Article  CAS  Google Scholar 

  54. de Vries SJ, Zacharias M (2012) Attract-EM: a new method for the computational assembly of large molecular machines using cryo-EM maps. PLoS One 7, e49733

    Article  PubMed  PubMed Central  Google Scholar 

  55. Schindler CEM, de Vries SJ, Zacharias M (2015) iATTRACT: simultaneous global and local interface optimization for protein-protein docking refinement. Proteins Struct Funct Bioinf 83:248–258

    Article  CAS  Google Scholar 

  56. Fiorucci S, Zacharias M (2010) Binding site prediction and improved scoring during flexible protein–protein docking with ATTRACT. Proteins Struct Funct Bioinf 78:3131–3139

    Article  CAS  Google Scholar 

  57. May A, Zacharias M (2008) Energy minimization in low-frequency normal modes to efficiently allow for global flexibility during systematic protein–protein docking. Proteins Struct Funct Bioinf 70:794–809

    Article  CAS  Google Scholar 

  58. London N, Movshovitz-Attias D, Schueler-Furman O (2010) The structural basis of peptide-protein binding strategies. Structure 18:188–199

    Article  CAS  PubMed  Google Scholar 

  59. Case D et al (2014) AMBER 14. University of California, San Francisco, CA

    Google Scholar 

  60. Rodrigues JPGLM et al (2012) Clustering biomolecular complexes by residue contacts similarity. Proteins Struct Funct Bioinf 80:1810–1817

    CAS  Google Scholar 

  61. Nilges M (1993) A calculation strategy for the structure determination of symmetric demers by 1H NMR. Proteins Struct Funct Bioinf 17:297–309

    Article  CAS  Google Scholar 

  62. Dominguez C, Boelens R, Bonvin AMJJ (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737

    Article  CAS  PubMed  Google Scholar 

  63. Vanhee P et al (2009) Protein-peptide interactions adopt the same structural motifs as monomeric protein folds. Structure 17:1128–1136

    Article  CAS  PubMed  Google Scholar 

  64. Watkins AM, Wuo MG, Arora PS (2015) Protein-protein interactions mediated by helical tertiary structure motifs. J Am Chem Soc 137:11622–11630

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Eswar N et al (2007) Comparative protein structure modeling using Modeller. Curr Protoc Protein Sci Chapter 2:Unit 2.9

    PubMed  Google Scholar 

  67. Kussie PH et al (1996) Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain. Science 274:948–953

    Article  CAS  PubMed  Google Scholar 

  68. Michelsen K et al (2012) Ordering of the N-terminus of human MDM2 by small molecule inhibitors. J Am Chem Soc 134:17059–17067

    Article  CAS  PubMed  Google Scholar 

  69. Chuikov S et al (2004) Regulation of p53 activity through lysine methylation. Nature 432:353–360

    Article  CAS  PubMed  Google Scholar 

  70. Avalos JL et al (2002) Structure of a Sir2 enzyme bound to an acetylated p53 peptide. Mol Cell 10:523–535

    Article  CAS  PubMed  Google Scholar 

  71. Kwon T et al (2003) Mechanism of histone lysine methyl transfer revealed by the structure of SET7/9AdoMet. EMBO J 22:292–303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins Struct Funct Bioinf 77:778–795

    Article  CAS  Google Scholar 

  73. Dosztanyi Z et al (2005) The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J Mol Biol 347:827–839

    Article  CAS  PubMed  Google Scholar 

  74. Dosztanyi Z et al (2005) IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 21:3433–3434

    Article  CAS  PubMed  Google Scholar 

  75. Vanhee P et al (2011) BriX: a database of protein building blocks for structural analysis, modeling and design. Nucleic Acids Res 39:D435–D442

    Article  CAS  PubMed  Google Scholar 

  76. Thevenet P et al (2012) PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res 40:W288–W293

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors are grateful for financial support from the Center for Integrated Protein Science Munich (CIPSM).

Author information

Authors and Affiliations

  1. Physics Department (T38), Center for Integrated Protein Science Munich (CIPSM), Technical University of Munich, James-Franck-Str. 1, 85748, Garching, Germany

    Christina Schindler & Martin Zacharias

Authors
  1. Christina Schindler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Zacharias
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Zacharias .

Editor information

Editors and Affiliations

  1. Microbiology and Molecular Genetics, Hebrew University Hadassah Medical School, Jerusalem, Israel

    Ora Schueler-Furman

  2. Organic Chemistry, The Weizmann Institute of Science, Rehovot, Israel

    Nir London

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Schindler, C., Zacharias, M. (2017). Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions. In: Schueler-Furman, O., London, N. (eds) Modeling Peptide-Protein Interactions. Methods in Molecular Biology, vol 1561. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6798-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6798-8_5

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6796-4

  • Online ISBN: 978-1-4939-6798-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation