Skip to main content
SpringerLink
Log in

Web-Based Computational Tools for the Prediction and Analysis of Posttranslational Modifications of Proteins

  • Protocol
  • First Online:
Post-Translational Modification of Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1934))

Abstract

The increase in the number of Web-based resources on posttranslational modification sites (PTMSs) in proteins is accelerating. This chapter presents a set of computational protocols describing how to work with the Internet resources when dealing with PTMSs. The protocols are intended for querying in PTMS-related databases, search of the PTMSs in the protein sequences and structures, and calculating the pI and molecular mass of the PTM isoforms. Thus, the modern bioinformatics prediction tools make it feasible to express protein modification in broader quantitative terms.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
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. Kaiser WM, Huber SC (2001) Post-translational regulation of nitrate reductase: mechanism, physiological relevance and environmental triggers. J Exp Bot 52:1981–1989

    Article  CAS  Google Scholar 

  2. Rocks O, Peyker A, Kahms M et al (2005) An acylation cycle regulates localization and activity of palmitoylated Ras isoforms. Science 307:1746–1752

    Article  CAS  Google Scholar 

  3. Goldberg AL (2003) Protein degradation and protection against misfolded or damaged proteins. Nature 426:895–899

    Article  CAS  Google Scholar 

  4. Tootle TL, Rebay I (2005) Post-translational modifications influence transcription factor activity: a view from the ETS superfamily. BioEssays 27:285–298

    Article  CAS  Google Scholar 

  5. McLachlin DT, Chait BT (2001) Analysis of phosphorylated proteins and peptides by mass spectrometry. Curr Opin Chem Biol 5:591–602

    Article  CAS  Google Scholar 

  6. Kemp BE, Pearson RB (1990) Protein kinase recognition sequence motifs. Trends Biochem Sci 15:342–346

    Article  CAS  Google Scholar 

  7. Persson B, Flinta C, von Heijne G, Jornvall H (1985) Structures of N-terminally acetylated proteins. Eur J Biochem 152:523–527

    Article  CAS  Google Scholar 

  8. Han KK, Martinage A (1992) Post-translational chemical modification(s) of proteins. Int J Biochem 24:19–28

    Article  CAS  Google Scholar 

  9. Hulo N, Sigrist CJ, Le Saux V et al (2004) Recent improvements to the PROSITE database. Nucleic Acids Res 32:D134–D137

    Article  CAS  Google Scholar 

  10. Blom N, Sicheritz-Ponten T, Gupta R et al (2004) Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 4:1633–1649

    Article  CAS  Google Scholar 

  11. Ivanisenko VA, Pintus SS, Grigorovich DA, Kolchanov NA (2005) PDBSite: a database of the 3D structure of protein functional sites. Nucleic Acids Res 33:D183–D187

    Article  CAS  Google Scholar 

  12. Sigrist CJA, de Castro E, Cerutti L et al (2012) New and continuing developments at PROSITE. Nucleic Acids Res 41(Database issue):D344–D347

    Article  Google Scholar 

  13. Diella F, Cameron S, Gemund C et al (2004) Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics 5:79

    Article  Google Scholar 

  14. Dinkel H, Chica C, Via A et al (2011) Phospho. ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res 39(suppl 1):D261–D267

    Article  CAS  Google Scholar 

  15. Vinayagam A, Pugalenthi G, Rajesh R, Sowdhamini R (2004) DSDBASE: a consortium of native and modelled disulphide bonds in proteins. Nucleic Acids Res 32:D200–D202

    Article  CAS  Google Scholar 

  16. Gupta R, Jung E, Gooley AA et al (1999) Scanning the available Dictyostelium discoideum proteome for O-linked GlcNAc glycosylation sites using neural networks. Glycobiology 9:1009–1022

    Article  CAS  Google Scholar 

  17. Garavelli JS (2004) The RESID database of protein modifications as a resource and annotation tool. Proteomics 4:1527–1533

    Article  CAS  Google Scholar 

  18. Gattiker A, Gasteiger E, Bairoch A (2002) ScanProsite: a reference implementation of a PROSITE scanning tool. Appl Bioinforma 1:107–108

    CAS  Google Scholar 

  19. De Castro E, Sigrist CJ, Gattiker A et al (2006) ScanProsite: detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins. Nucleic Acids Res 34(suppl 2):W362–W365

    Article  Google Scholar 

  20. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294:1351–1362

    Article  CAS  Google Scholar 

  21. Obenauer JC, Cantley LC, Yaffe MB (2003) Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res 31:3635–3641

    Article  CAS  Google Scholar 

  22. Liu Y, Lin Y (2004) A novel method for N-terminal acetylation prediction. Genomics Proteomics Bioinformatics 2:253–255

    Article  CAS  Google Scholar 

  23. Chen H, Xue Y, Huang N et al (2006) MeMo: a web tool for prediction of protein methylation modifications. Nucleic Acids Res 34(suppl 2):W249–W253

    Article  CAS  Google Scholar 

  24. Bhasin M, Zhang H, Reinherz EL, Reche PA (2005) Prediction of methylated CpGs in DNA sequences using a support vector machine. FEBS Lett 579(20):4302–4308

    Article  CAS  Google Scholar 

  25. Julenius K, Molgaard A, Gupta R, Brunak S (2005) Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites. Glycobiology 15:153–164

    Article  CAS  Google Scholar 

  26. Steentoft C, Vakhrushev SY, Joshi HJ et al (2013) Precision mapping of the human O GalNAc glycoproteome through SimpleCell technology. EMBO J 32(10):1478–1488

    Article  CAS  Google Scholar 

  27. Eisenhaber B, Bork P, Eisenhaber F (1999) Prediction of potential GPI-modification sites in proprotein sequences. J Mol Biol 292:741–758

    Article  CAS  Google Scholar 

  28. Monigatti F, Gasteiger E, Bairoch A, Jung E (2002) The Sulfinator: predicting tyrosine sulfation sites in protein sequences. Bioinformatics 18:769–770

    Article  CAS  Google Scholar 

  29. Baldi P, Cheng J, Vullo A (2005) Large-scale prediction of disulphide bond connectivity. In: Saul L, Weiss Y, Bottou L (eds) Advances in neural information processing systems (NIPS 2004), vol 17. MIT press, Cambridge, MA, pp 97–104

    Google Scholar 

  30. Ivanisenko VA, Pintus SS, Grigorovich DA, Kolchanov NA (2004) PDBSiteScan: a program for searching for active, binding and post-translational modification sites in the 3D structures of proteins. Nucleic Acids Res 32:W549–W554

    Article  CAS  Google Scholar 

  31. Halligan BD, Ruotti V, Jin W et al (2004) ProMoST (protein modification screening tool): a web-based tool for mapping protein modifications on two-dimensional gels. Nucleic Acids Res 32:W638–W644

    Article  CAS  Google Scholar 

  32. Halligan BD (2009) ProMoST: a tool for calculating the pI and molecular mass of phosphorylated and modified proteins on two-dimensional gels. In: Phospho-proteomics. Humana Press, New York, pp 283–298

    Chapter  Google Scholar 

  33. Sayle RA, Milner-White EJ (1995) RasMol: biomolecular graphics for all. Trends Biochem Sci 20:374–376

    Article  CAS  Google Scholar 

  34. Martz E (2002) Protein explorer: easy yet powerful macromolecular visualization. Trends Biochem Sci 27(2):107–109

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The work was performed with the support of the State Budgeted Project No 0324-2019-0040 “Genetic basis of biotechnology and bioinformatics”.

Author information

Authors and Affiliations

  1. Institute of Cytology and Genetics SB RAS, Novosibirsk State University, Novosibirsk, Russia

    Vladimir A. Ivanisenko, Timofey V. Ivanisenko, Olga V. Saik, Pavel S. Demenkov, Dmitry A. Afonnikov & Nikolay A. Kolchanov

Authors
  1. Vladimir A. Ivanisenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timofey V. Ivanisenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olga V. Saik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pavel S. Demenkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dmitry A. Afonnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nikolay A. Kolchanov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir A. Ivanisenko .

Editor information

Editors and Affiliations

  1. Recombinant Research and Development, Octapharma Biopharmaceuticals GmbH, Heidelberg, Germany

    Christoph Kannicht

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Ivanisenko, V.A., Ivanisenko, T.V., Saik, O.V., Demenkov, P.S., Afonnikov, D.A., Kolchanov, N.A. (2019). Web-Based Computational Tools for the Prediction and Analysis of Posttranslational Modifications of Proteins. In: Kannicht, C. (eds) Post-Translational Modification of Proteins. Methods in Molecular Biology, vol 1934. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9055-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9055-9_1

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9053-5

  • Online ISBN: 978-1-4939-9055-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation