Skip to main content
SpringerLink
Log in

Bioinformatics Analysis of Functional Associations of PTMs

  • Protocol
  • First Online:
Protein Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1558))

Abstract

Post-translational modifications (PTMs) are an important source of protein regulation; they fine-tune the function, localization, and interaction with other molecules of the majority of proteins and are partially responsible for their multifunctionality. Usually, proteins have several potential modification sites, and their patterns of occupancy are associated with certain functional states. These patterns imply cross talk among PTMs within and between proteins, the majority of which are still to be discovered. Several methods detect associations between PTMs; these have recently combined into a global resource, the PTMcode database, which contains already known and predicted functional associations between pairs of PTMs from more than 45,000 proteins in 19 eukaryotic species.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
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. Minguez P, Letunic I, Parca L et al (2015) PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins. Nucleic Acids Res 43:D494–D502. doi:10.1093/nar/gku1081

    Article  PubMed  Google Scholar 

  2. Gnad F, Gunawardena J, Mann M (2010) PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res 39:D253–D260. doi:10.1093/nar/gkq1159

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lu C-T, Huang K-Y, Su M-G et al (2013) DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications. Nucleic Acids Res 41:D295–D305. doi:10.1093/nar/gks1229

    Article  CAS  PubMed  Google Scholar 

  4. Sadowski I, Breitkreutz B-J, Stark C et al. (2013) The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database (Oxford) 2013:bat026. doi:10.1093/database/bat026

  5. Naegle KM, Gymrek M, Joughin BA et al (2010) PTMScout, a web resource for analysis of high throughput post-translational proteomics studies. Mol Cell Proteomics 9:2558–2570. doi:10.1074/mcp.M110.001206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Craveur P, Rebehmed J, de Brevern AG (2014) PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins. Database (Oxford) 2014:bau041. doi:10.1093/database/bau041

  7. Hunter T (2007) The age of crosstalk: phosphorylation, ubiquitination, and beyond. Mol Cell 28:730–738. doi:10.1016/j.molcel.2007.11.019

    Article  CAS  PubMed  Google Scholar 

  8. Beltrao P, Trinidad JC, Fiedler D et al (2009) Evolution of phosphoregulation: comparison of phosphorylation patterns across yeast species. PLoS Biol 7:e1000134. doi:10.1371/journal.pbio.1000134

    Article  PubMed  PubMed Central  Google Scholar 

  9. Minguez P, Parca L, Diella F et al (2012) Deciphering a global network of functionally associated post-translational modifications. Mol Syst Biol 8:599. doi:10.1038/msb.2012.31

    Article  PubMed  PubMed Central  Google Scholar 

  10. The UniProt Consortium (2014) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212. doi:10.1093/nar/gku989

    Article  PubMed Central  Google Scholar 

  11. Hornbeck PV, Zhang B, Murray B et al (2015) PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43:D512–D520. doi:10.1093/nar/gku1267

    Article  PubMed  Google Scholar 

  12. Filtz TM, Vogel WK, Leid M (2014) Regulation of transcription factor activity by interconnected post-translational modifications. Trends Pharmacol Sci 35:76–85. doi:10.1016/j.tips.2013.11.005

    Article  CAS  PubMed  Google Scholar 

  13. Sun B, Zhang M, Cui P et al (2015) Nonsynonymous single-nucleotide variations on some posttranslational modifications of human proteins and the association with diseases. Comput Math Methods Med 2015:124630. doi:10.1155/2015/124630

    PubMed  PubMed Central  Google Scholar 

  14. Duan G, Walther D (2015) The roles of post-translational modifications in the context of protein interaction networks. PLoS Comput Biol 11:e1004049. doi:10.1371/journal.pcbi.1004049

    Article  PubMed  PubMed Central  Google Scholar 

  15. Park CY, Krishnan A, Zhu Q et al (2014) Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms. Bioinformatics 31:1093–1101. doi:10.1093/bioinformatics/btu786

    Article  PubMed  PubMed Central  Google Scholar 

  16. von Appen A, Kosinski J, Sparks L et al (2015) In situ structural analysis of the human nuclear pore complex. Nature 526:140–143. doi:10.1038/nature15381

    Article  Google Scholar 

  17. Huang Y, Xu B, Zhou X et al (2015) Systematic characterization and prediction of post-translational modification cross-talk. Mol Cell Proteomics 14:761–770. doi:10.1074/mcp.M114.037994

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Woodsmith J, Kamburov A, Stelzl U (2013) Dual coordination of post translational modifications in human protein networks. PLoS Comput Biol 9:e1002933. doi:10.1371/journal.pcbi.1002933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Creixell P, Linding R (2012) Cells, shared memory and breaking the PTM code. Mol Syst Biol 8:598

    Article  PubMed  PubMed Central  Google Scholar 

  20. Dinkel H, Chica C, Via A et al (2011) Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res 39:D261–D267. doi:10.1093/nar/gkq1104

    Article  CAS  PubMed  Google Scholar 

  21. Orchard S, Ammari M, Aranda B et al (2014) The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42:D358–D363. doi:10.1093/nar/gkt1115

    Article  CAS  PubMed  Google Scholar 

  22. Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human protein reference database—2009 update. Nucleic Acids Res 37:D767–D772. doi:10.1093/nar/gkn892

    Article  CAS  PubMed  Google Scholar 

  23. Szklarczyk D, Franceschini A, Wyder S et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452. doi:10.1093/nar/gku1003

    Article  PubMed  Google Scholar 

  24. The UniProt Consortium (2014) Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res 42:D191–D198. doi:10.1093/nar/gkt1140

    Article  Google Scholar 

  25. Danielsen JMR, Sylvestersen KB, Bekker-Jensen S et al (2011) Mass spectrometric analysis of lysine ubiquitylation reveals promiscuity at site level. Mol Cell Proteomics 10:M110.003590. doi:10.1074/mcp.M110.003590

    Article  PubMed  Google Scholar 

  26. Choudhary C, Kumar C, Gnad F et al (2009) Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325:834–840. doi:10.1126/science.1175371

    Article  CAS  PubMed  Google Scholar 

  27. Henriksen P, Wagner SA, Weinert BT et al (2012) Proteome-wide analysis of lysine acetylation suggests its broad regulatory scope in Saccharomyces cerevisiae. Mol Cell Proteomics 11:1510–1522. doi:10.1074/mcp.M112.017251

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lundby A, Secher A, Lage K et al (2012) Quantitative maps of protein phosphorylation sites across 14 different rat organs and tissues. Nat Commun 3:876. doi:10.1038/ncomms1871

    Article  PubMed  PubMed Central  Google Scholar 

  29. Matic I, Schimmel J, Hendriks IA et al (2010) Site-specific identification of SUMO-2 targets in cells reveals an inverted SUMOylation motif and a hydrophobic cluster SUMOylation motif. Mol Cell 39:641–652. doi:10.1016/j.molcel.2010.07.026

    Article  CAS  PubMed  Google Scholar 

  30. Murray CI, Kane LA, Uhrigshardt H et al (2011) Site-mapping of in vitro S-nitrosation in cardiac mitochondria: implications for cardioprotection. Mol Cell Proteomics 10:M110.004721. doi:10.1074/mcp.M110.004721

    Article  PubMed  Google Scholar 

  31. Weinert BT, Wagner SA, Horn H et al (2011) Proteome-wide mapping of the Drosophila acetylome demonstrates a high degree of conservation of lysine acetylation. Sci Signal 4:ra48. doi:10.1126/scisignal.2001902

    Article  CAS  PubMed  Google Scholar 

  32. Zielinska DF, Gnad F, Schropp K et al (2012) Mapping N-glycosylation sites across seven evolutionarily distant species reveals a divergent substrate proteome despite a common core machinery. Mol Cell 46:542–548. doi:10.1016/j.molcel.2012.04.031

    Article  CAS  PubMed  Google Scholar 

  33. Wagner SA, Beli P, Weinert BT et al (2011) A proteome-wide, quantitative survey of in vivo ubiquitylation sites reveals widespread regulatory roles. Mol Cell Proteomics 10:M111.013284. doi:10.1074/mcp.M111.013284

    Article  PubMed  PubMed Central  Google Scholar 

  34. Powell S, Forslund K, Szklarczyk D et al (2014) eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res 42:D231–D239. doi:10.1093/nar/gkt1253

    Article  CAS  PubMed  Google Scholar 

  35. Franceschini A, Szklarczyk D, Frankild S et al (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:D808–D815. doi:10.1093/nar/gks1094

    Article  CAS  PubMed  Google Scholar 

  36. Boekhorst J, van Breukelen B, Heck A, Snel B (2008) Comparative phosphoproteomics reveals evolutionary and functional conservation of phosphorylation across eukaryotes. Genome Biol 9:R144. doi:10.1186/gb-2008-9-10-r144

    Article  PubMed  PubMed Central  Google Scholar 

  37. Chen SC-C, Chen F-C, Li W-H (2010) Phosphorylated and nonphosphorylated serine and threonine residues evolve at different rates in mammals. Mol Biol Evol 27:2548–2554. doi:10.1093/molbev/msq142

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tan CSH, Bodenmiller B, Pasculescu A et al (2009) Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. Sci Signal 2:ra39. doi:10.1126/scisignal.2000316

    Article  PubMed  Google Scholar 

  39. Humphrey SJ, James DE, Mann M (2015) Protein phosphorylation: a major switch mechanism for metabolic regulation. Trends Endocrinol Metab 26:676–687. doi:10.1016/j.tem.2015.09.013

    Article  CAS  PubMed  Google Scholar 

  40. Byeon I-JL, Li H, Song H et al (2005) Sequential phosphorylation and multisite interactions characterize specific target recognition by the FHA domain of Ki67. Nat Struct Mol Biol 12:987–993. doi:10.1038/nsmb1008

    Article  CAS  PubMed  Google Scholar 

  41. de Juan D, Pazos F, Valencia A (2013) Emerging methods in protein co-evolution. Nat Rev Genet 14:249–261. doi:10.1038/nrg3414

    Article  PubMed  Google Scholar 

  42. Cover TM, Thomas JA (1991) Elements of information theory. John Wiley & Sons, New York

    Book  Google Scholar 

  43. Martin LC, Gloor GB, Dunn SD, Wahl LM (2005) Using information theory to search for co-evolving residues in proteins. Bioinformatics 21:4116–4124. doi:10.1093/bioinformatics/bti671

    Article  CAS  PubMed  Google Scholar 

  44. Skerker JM, Perchuk BS, Siryaporn A et al (2008) Rewiring the specificity of two-component signal transduction systems. Cell 133:1043–1054. doi:10.1016/j.cell.2008.04.040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Pietal MJ, Bujnicki JM, Kozlowski LP (2015) GDFuzz3D: a method for protein 3D structure reconstruction from contact maps, based on a non-Euclidean distance function. Bioinformatics 31:3499–3505. doi:10.1093/bioinformatics/btv390

    Article  CAS  PubMed  Google Scholar 

  46. Berman HM, Kleywegt GJ, Nakamura H, Markley JL (2012) The future of the protein data bank. Biopolymers. doi:10.1002/bip.22132

  47. Seet BT, Dikic I, Zhou M-M, Pawson T (2006) Reading protein modifications with interaction domains. Nat Rev Mol Cell Biol 7:473–483. doi:10.1038/nrm1960

    Article  CAS  PubMed  Google Scholar 

  48. Hart GW, Greis KD, Dong LY et al (1995) O-linked N-acetylglucosamine: the “yin-yang” of Ser/Thr phosphorylation? Nuclear and cytoplasmic glycosylation. Adv Exp Med Biol 376:115–123

    Article  CAS  PubMed  Google Scholar 

  49. Latham JA, Dent SYR (2007) Cross-regulation of histone modifications. Nat Struct Mol Biol 14:1017–1024. doi:10.1038/nsmb1307

    Article  CAS  PubMed  Google Scholar 

  50. Brooks CL, Gu W (2003) Ubiquitination, phosphorylation and acetylation: the molecular basis for p53 regulation. Curr Opin Cell Biol 15:164–171. doi:10.1016/S0955-0674(03)00003-6

    Article  CAS  PubMed  Google Scholar 

  51. Beltrao P, Albanèse V, Kenner LR et al (2012) Systematic functional prioritization of protein posttranslational modifications. Cell 150:413–425. doi:10.1016/j.cell.2012.05.036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Zeidan Q, Hart GW (2010) The intersections between O-GlcNAcylation and phosphorylation: implications for multiple signaling pathways. J Cell Sci 123:13–22. doi:10.1242/jcs.053678

    Article  CAS  PubMed  Google Scholar 

  53. Butt AM, Khan IB, Hussain M et al (2011) Role of post translational modifications and novel crosstalk between phosphorylation and O-beta-GlcNAc modifications in human claudin-1, −3 and −4. Mol Biol Rep 39:1359–1369. doi:10.1007/s11033-011-0870-7

    Article  PubMed  Google Scholar 

  54. Vodermaier HC (2004) APC/C and SCF: controlling each other and the cell cycle. Curr Biol 14:R787–R796. doi:10.1016/j.cub.2004.09.020

    Article  CAS  PubMed  Google Scholar 

  55. Letunic I, Doerks T, Bork P (2015) SMART: recent updates, new developments and status in 2015. Nucleic Acids Res 43:D257–D260. doi:10.1093/nar/gku949

    Article  PubMed  Google Scholar 

  56. Lienhard GE (2008) Non-functional phosphorylations? Trends Biochem Sci 33:351–352. doi:10.1016/j.tibs.2008.05.004

    Article  CAS  PubMed  Google Scholar 

  57. Wang Z, Ding G, Geistlinger L et al (2011) Evolution of protein phosphorylation for distinct functional modules in vertebrate genomes. Mol Biol Evol 28:1131–1140. doi:10.1093/molbev/msq268

    Article  CAS  PubMed  Google Scholar 

  58. Gnad F, Ren S, Cox J et al (2007) PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol 8:R250. doi:10.1186/gb-2007-8-11-r250

    Article  PubMed  PubMed Central  Google Scholar 

  59. Holt LJ, Tuch BB, Villén J et al (2009) Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution. Science 325:1682–1686. doi:10.1126/science.1172867

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Tan CSH, Bader GD (2012) Phosphorylation sites of higher stoichiometry are more conserved. Nat Methods 9:317. doi:10.1038/nmeth.1941

    Article  CAS  PubMed  Google Scholar 

  61. Zielinska DF, Gnad F, Wiśniewski JR, Mann M (2010) Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell 141:897–907. doi:10.1016/j.cell.2010.04.012

    Article  CAS  PubMed  Google Scholar 

  62. Martínez-Ruiz A, Lamas S (2004) S-nitrosylation: a potential new paradigm in signal transduction. Cardiovasc Res 62:43–52. doi:10.1016/j.cardiores.2004.01.013

    Article  PubMed  Google Scholar 

  63. Linding R, Jensen LJ, Diella F et al (2003) Protein disorder prediction: implications for structural proteomics. Structure 11:1453–1459

    Article  CAS  PubMed  Google Scholar 

  64. Ullah S, Lin S, Xu Y et al (2016) dbPAF: an integrative database of protein phosphorylation in animals and fungi. Sci Rep 6:23534. doi:10.1038/srep23534

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Pan Z, Liu Z, Cheng H et al (2014) Systematic analysis of the in situ crosstalk of tyrosine modifications reveals no additional natural selection on multiply modified residues. Sci Rep 4:7331. doi:10.1038/srep07331

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wang ZA, Singh D, van der Wel H, West CM (2011) Prolyl hydroxylation- and glycosylation-dependent functions of Skp1 in O2-regulated development of Dictyostelium. Dev Biol 349:283–295. doi:10.1016/j.ydbio.2010.10.013

    Article  CAS  PubMed  Google Scholar 

  67. Yachie N, Saito R, Sugiyama N et al (2011) Integrative features of the yeast phosphoproteome and protein–protein interaction map. PLoS Comput Biol 7:e1001064. doi:10.1371/journal.pcbi.1001064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Genetics and Genomics, Instituto de Investigacion Sanitaria-University Hospital Fundacion Jimenez Diaz (IIS-FJD), Avda. Reyes Católicos 2, 28040, Madrid, Spain

    Pablo Minguez

  2. European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117, Heidelberg, Germany

    Peer Bork

  3. Max Delbrück Centre for Molecular Medicine, 13125, Berlin, Germany

    Peer Bork

Authors
  1. Pablo Minguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peer Bork
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo Minguez .

Editor information

Editors and Affiliations

  1. Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, USA

    Cathy H. Wu

  2. Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, USA

    Cecilia N. Arighi

  3. Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA

    Karen E. Ross

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Minguez, P., Bork, P. (2017). Bioinformatics Analysis of Functional Associations of PTMs. In: Wu, C., Arighi, C., Ross, K. (eds) Protein Bioinformatics. Methods in Molecular Biology, vol 1558. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6783-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6783-4_14

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6781-0

  • Online ISBN: 978-1-4939-6783-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation