Skip to main content
SpringerLink
Log in

De Novo Peptide Structure Prediction: An Overview

  • Protocol
  • First Online:
Computational Peptidology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1268))

Abstract

Peptide structure identification is an important contribution to the further characterization of the residues involved in functional interactions. De novo structure peptide prediction has, in the past few years, made significant progresses that make reasonable, for peptides up to 50 amino acids, its use for the fast identification of their structural topologies. Here, we introduce some of the concepts underlying approaches of the field, together with their limits.

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

Access this chapter

Log in via an institution
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
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. Williams AD, Shivaprasad S, Wetzel R (2006) Alanine scanning mutagenesis of Abeta(1–40) amyloid fibril stability. J Mol Biol 357(4):1283–1294

    Article  CAS  PubMed  Google Scholar 

  2. Van Craenenbroeck M, Gregoire F, De Neef P et al (2004) Ala-scan of ghrelin (1–14): interaction with the recombinant human ghrelin receptor. Peptides 25(6):959–965

    Article  PubMed  Google Scholar 

  3. Vanhee P, van der Sloot AM, Verschueren E et al (2011) Computational design of peptide ligands. Trends Biotechnol 29(5):231–239

    Article  CAS  PubMed  Google Scholar 

  4. Audie J, Boyd C (2010) The synergistic use of computation, chemistry and biology to discover novel peptide-based drugs: the time is right. Curr Pharm Des 16(5):567–582

    Article  CAS  PubMed  Google Scholar 

  5. Berman HM, Westbrook J, Feng Z et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Kryshtafovych A, Monastyrskyy B, Fidelis K (2013) CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL. Proteins 82(Suppl 2):7–13

    PubMed  Google Scholar 

  7. Marti-Renom MA, Stuart AC, Fiser A et al (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325

    Article  CAS  PubMed  Google Scholar 

  8. Gehrmann J, Alewood PF, Craik DJ (1998) Structure determination of the three disulfide bond isomers of alpha-conotoxin GI: a model for the role of disulfide bonds in structural stability. J Mol Biol 278(2):401–415

    Article  CAS  PubMed  Google Scholar 

  9. Loening NM, Wilson ZN, Zobel-Thropp PA et al (2013) Solution structures of two homologous venom peptides from Sicarius dolichocephalus. PLoS One 8(1):e54401

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Conibear AC, Rosengren KJ, Daly NL et al (2013) The cyclic cystine ladder in theta-defensins is important for structure and stability, but not antibacterial activity. J Biol Chem 288(15):10830–10840

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Duan Y, Kollman PA (1998) Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282(5389):740–744

    Article  CAS  PubMed  Google Scholar 

  12. Zagrovic B, Snow CD, Shirts MR et al (2002) Simulation of folding of a small alpha-helical protein in atomistic detail using worldwide-distributed computing. J Mol Biol 323(5):927–937

    Article  CAS  PubMed  Google Scholar 

  13. Simmerling C, Strockbine B, Roitberg AE (2002) All-atom structure prediction and folding simulations of a stable protein. J Am Chem Soc 124(38):11258–11259

    Article  CAS  PubMed  Google Scholar 

  14. Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314(1–2):141–151

    Article  CAS  Google Scholar 

  15. Sanbonmatsu KY, Garcia AE (2002) Structure of Met-enkephalin in explicit aqueous solution using replica exchange molecular dynamics. Proteins 46(2):225–234

    Article  CAS  PubMed  Google Scholar 

  16. Rhee YM, Pande VS (2003) Multiplexed-replica exchange molecular dynamics method for protein folding simulation. Biophys J 84(2 Pt 1):775–786

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Im W, Brooks CL III (2004) De novo folding of membrane proteins: an exploration of the structure and NMR properties of the fd coat protein. J Mol Biol 337(3):513–519

    Article  CAS  PubMed  Google Scholar 

  18. Im W, Brooks CL III (2005) Interfacial folding and membrane insertion of designed peptides studied by molecular dynamics simulations. Proc Natl Acad Sci U S A 102(19):6771–6776

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Shaw DE, Maragakis P, Lindorff-Larsen K et al (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330(6002):341–346

    Article  CAS  PubMed  Google Scholar 

  20. Lindorff-Larsen K, Piana S, Dror RO et al (2011) How fast-folding proteins fold. Science 334(6055):517–520

    Article  CAS  PubMed  Google Scholar 

  21. Vetter I, Davis JL, Rash LD et al (2011) Venomics: a new paradigm for natural products-based drug discovery. Amino Acids 40(1):15–28

    Article  CAS  PubMed  Google Scholar 

  22. Ishikawa K, Yue K, Dill KA (1999) Predicting the structures of 18 peptides using Geocore. Protein Sci 8(4):716–721

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Kaur H, Garg A, Raghava GP (2007) PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept Lett 14(7):626–631

    Article  CAS  PubMed  Google Scholar 

  24. Jones DT (1999) GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 287(4):797–815

    Article  CAS  PubMed  Google Scholar 

  25. Kaur H, Raghava GP (2004) A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics 20(16):2751–2758

    Article  CAS  PubMed  Google Scholar 

  26. Jayaram B, Bhushan K, Shenoy SR et al (2006) Bhageerath: an energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins. Nucleic Acids Res 34(21):6195–6204

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Jayaram B, Dhingra P, Lakhani B (2012) Bhageerath-targeting the near impossible: pushing the frontiers of atomic models for protein tertiary structure prediction. J Chem Sci 124(1):83–91

    Article  CAS  Google Scholar 

  28. Thomas A, Deshayes S, Decaffmeyer M et al (2009) PepLook: an innovative in silico tool for determination of structure, polymorphism and stability of peptides. Adv Exp Med Biol 611:459–460

    Article  CAS  PubMed  Google Scholar 

  29. Maupetit J, Derreumaux P, Tuffery P (2009) PEP-FOLD: an online resource for de novo peptide structure prediction. Nucleic Acids Res 37(Web Server issue):W498–W503

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Maupetit J, Derreumaux P, Tuffery P (2010) A fast method for large-scale de novo peptide and miniprotein structure prediction. J Comput Chem 31(4):726–738

    CAS  PubMed  Google Scholar 

  31. Etchebest C, Benros C, Hazout S et al (2005) A structural alphabet for local protein structures: improved prediction methods. Proteins 59(4):810–827

    Article  CAS  PubMed  Google Scholar 

  32. Glick M, Rayan A, Goldblum A (2002) A stochastic algorithm for global optimization and for best populations: a test case of side chains in proteins. Proc Natl Acad Sci U S A 99(2):703–708

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Beaufays J, Lins L, Thomas A et al (2012) In silico predictions of 3D structures of linear and cyclic peptides with natural and non-proteinogenic residues. J Pept Sci 18(1):17–24

    Article  CAS  PubMed  Google Scholar 

  34. Camproux AC, Gautier R, Tuffery P (2004) A hidden Markov model derived structural alphabet for proteins. J Mol Biol 339(3):591–605

    Article  CAS  PubMed  Google Scholar 

  35. Tuffery P, Guyon F, Derreumaux P (2005) Improved greedy algorithm for protein structure reconstruction. J Comput Chem 26(5):506–513

    Article  CAS  PubMed  Google Scholar 

  36. Maupetit J, Tuffery P, Derreumaux P (2007) A coarse-grained protein force field for folding and structure prediction. Proteins 69(2):394–408

    Article  CAS  PubMed  Google Scholar 

  37. Thevenet P, Shen Y, Maupetit J et al (2012) PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res 40(Web Server issue):W288–W293

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Simons KT, Bonneau R, Ruczinski I et al (1999) Ab initio protein structure prediction of CASP III targets using ROSETTA. Proteins Suppl 3:171–176

    Google Scholar 

  39. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9:40

    Article  PubMed Central  PubMed  Google Scholar 

  40. Thévenet P, Shen Y, Maupetit J et al (2012) Delivering the native structures of peptides from computer simulations and predicted NMR proton chemical shifts. In: Abstract of 32nd European Peptides Society Symposium, Megaron, Athens, Greece, 2–8 Sept 2012

    Google Scholar 

  41. Thevenet P, Tuffery P. submitted

    Google Scholar 

  42. Fan C, Cheng S, Sinha S et al (2012) Interactions between the termini of lumen enzymes and shell proteins mediate enzyme encapsulation into bacterial microcompartments. Proc Natl Acad Sci U S A 109(37):14995–15000

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Charlois Y, Lins L, Brasseur R (2011) A new in-silico method for determination of helical transmembrane domains based on the PepLook scan: application to IL-2Rbeta and IL-2Rgammac receptor chains. BMC Struct Biol 11:26

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Steckbeck JD, Craigo JK, Barnes CO et al (2011) Highly conserved structural properties of the C-terminal tail of HIV-1 gp41 protein despite substantial sequence variation among diverse clades: implications for functions in viral replication. J Biol Chem 286(31):27156–27166

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Berges R, Balzeau J, Takahashi M et al (2012) Structure-function analysis of the glioma targeting NFL-TBS.40-63 peptide corresponding to the tubulin-binding site on the light neurofilament subunit. PLoS One 7(11):e49436. doi:10.1371/journal.pone.0049436, PONE-D-12-10940 [pii]

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Liu Z, Dominy BN, Shakhnovich EI (2004) Structural mining: self-consistent design on flexible protein-peptide docking and transferable binding affinity potential. J Am Chem Soc 126(27):8515–8528

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work has been supported by the French IA bioinformatics BipBip grant, by INSERM UMR-S 973 recurrent funding.

Author information

Authors and Affiliations

  1. Molécules Thérapeutiques In Silico, Inserm UMR-S 973, Université Paris Diderot, Sorbonne Paris Cité, 35 rue Helene Brion, 75013, Paris, France

    Pierre Thévenet, Julien Rey, Gautier Moroy & Pierre Tuffery

Authors
  1. Pierre Thévenet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julien Rey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gautier Moroy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pierre Tuffery
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pierre Tuffery .

Editor information

Editors and Affiliations

  1. Center of Bioinformatics (COBI), School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Peng Zhou

  2. Center of Bioinformatics (COBI), School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China

    Jian Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Thévenet, P., Rey, J., Moroy, G., Tuffery, P. (2015). De Novo Peptide Structure Prediction: An Overview. In: Zhou, P., Huang, J. (eds) Computational Peptidology. Methods in Molecular Biology, vol 1268. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2285-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2285-7_1

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2284-0

  • Online ISBN: 978-1-4939-2285-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation