Skip to main content
SpringerLink
Log in

Computational Methods to Predict Intrinsically Disordered Regions and Functional Regions in Them

  • Protocol
  • First Online:
Homology Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2627))

  • 655 Accesses

Abstract

Intrinsically disordered regions (IDRs) are protein regions that do not adopt fixed tertiary structures. Since these regions lack ordered three-dimensional structures, they should be excluded from the target portions of homology modeling. IDRs can be predicted from the amino acid sequences, because their amino acid compositions are different from that of the structured domains. This chapter provides a review of the prediction methods of IDRs and a case study of IDR prediction.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

References

  1. Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293(2):321–331. https://doi.org/10.1006/jmbi.1999.3110

    Article  CAS  PubMed  Google Scholar 

  2. Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z (2002) Intrinsic disorder and protein function. Biochemistry 41(21):6573–6582. https://doi.org/10.1021/bi012159+

    Article  CAS  PubMed  Google Scholar 

  3. Tompa P (2005) The interplay between structure and function in intrinsically unstructured proteins. FEBS Lett 579(15):3346–3354. https://doi.org/10.1016/j.febslet.2005.03.072

    Article  CAS  PubMed  Google Scholar 

  4. Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337(3):635–645. https://doi.org/10.1016/j.jmb.2004.02.002

    Article  CAS  PubMed  Google Scholar 

  5. Haynes C, Oldfield CJ, Ji F, Klitgord N, Cusick ME, Radivojac P, Uversky VN, Vidal M, Iakoucheva LM (2006) Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes. PLoS Comput Biol 2(8):e100. https://doi.org/10.1371/journal.pcbi.0020100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ota M, Gonja H, Koike R, Fukuchi S (2016) Multiple-localization and hub proteins. PLoS One 11(6):e0156455. https://doi.org/10.1371/journal.pone.0156455

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Koike R, Amano M, Kaibuchi K, Ota M (2020) Protein kinases phosphorylate long disordered regions in intrinsically disordered proteins. Protein Sci 29(2):564–571. https://doi.org/10.1002/pro.3789

    Article  CAS  PubMed  Google Scholar 

  8. Iakoucheva LM, Brown CJ, Lawson JD, Obradović Z, Dunker AK (2002) Intrinsic disorder in cell-signaling and cancer-associated proteins. J Mol Biol 323(3):573–584. https://doi.org/10.1016/s0022-2836(02)00969-5

    Article  CAS  PubMed  Google Scholar 

  9. Mohan A, Oldfield CJ, Radivojac P, Vacic V, Cortese MS, Dunker AK, Uversky VN (2006) Analysis of molecular recognition features (MoRFs). J Mol Biol 362(5):1043–1059. https://doi.org/10.1016/j.jmb.2006.07.087

    Article  CAS  PubMed  Google Scholar 

  10. Ren S, Uversky VN, Chen Z, Dunker AK, Obradovic Z (2008) Short Linear Motifs recognized by SH2, SH3 and Ser/Thr Kinase domains are conserved in disordered protein regions. BMC Genomics 9 Suppl 2(Suppl 2):S26. https://doi.org/10.1186/1471-2164-9-s2-s26

    Article  PubMed  Google Scholar 

  11. Schad E, Ficho E, Pancsa R, Simon I, Dosztanyi Z, Meszaros B (2018) DIBS: a repository of disordered binding sites mediating interactions with ordered proteins. Bioinformatics 34(3):535–537. https://doi.org/10.1093/bioinformatics/btx640

    Article  CAS  PubMed  Google Scholar 

  12. Fukuchi S, Sakamoto S, Nobe Y, Murakami SD, Amemiya T, Hosoda K, Koike R, Hiroaki H, Ota M (2012) IDEAL: intrinsically disordered proteins with extensive annotations and literature. Nucleic Acids Res 40(Database issue):D507–D511. https://doi.org/10.1093/nar/gkr884

    Article  CAS  PubMed  Google Scholar 

  13. Canadillas JM, Tidow H, Freund SM, Rutherford TJ, Ang HC, Fersht AR (2006) Solution structure of p53 core domain: structural basis for its instability. Proc Natl Acad Sci U S A 103(7):2109–2114. https://doi.org/10.1073/pnas.0510941103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lee W, Harvey TS, Yin Y, Yau P, Litchfield D, Arrowsmith CH (1994) Solution structure of the tetrameric minimum transforming domain of p53. Nat Struct Biol 1(12):877–890. https://doi.org/10.1038/nsb1294-877

    Article  CAS  PubMed  Google Scholar 

  15. Dawson R, Müller L, Dehner A, Klein C, Kessler H, Buchner J (2003) The N-terminal domain of p53 is natively unfolded. J Mol Biol 332(5):1131–1141. https://doi.org/10.1016/j.jmb.2003.08.008

    Article  CAS  PubMed  Google Scholar 

  16. Rustandi RR, Baldisseri DM, Weber DJ (2000) Structure of the negative regulatory domain of p53 bound to S100B(betabeta). Nat Struct Biol 7(7):570–574. https://doi.org/10.1038/76797

    Article  CAS  PubMed  Google Scholar 

  17. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402. https://doi.org/10.1093/nar/25.17.3389

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Uversky VN, Gillespie JR, Fink AL (2000) Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins 41(3):415–427. https://doi.org/10.1002/1097-0134(20001115)41:3<415::aid-prot130>3.0.co;2-7

    Article  CAS  PubMed  Google Scholar 

  19. Hemmings HC Jr, Nairn AC, Aswad DW, Greengard P (1984) DARPP-32, a dopamine- and adenosine 3′:5′-monophosphate-regulated phosphoprotein enriched in dopamine-innervated brain regions. II. Purification and characterization of the phosphoprotein from bovine caudate nucleus. J Neurosci 4(1):99–110. https://doi.org/10.1523/jneurosci.04-01-00099.1984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Weinreb PH, Zhen W, Poon AW, Conway KA, Lansbury PT Jr (1996) NACP, a protein implicated in Alzheimer’s disease and learning, is natively unfolded. Biochemistry 35(43):13709–13715. https://doi.org/10.1021/bi961799n

    Article  CAS  PubMed  Google Scholar 

  21. Prilusky J, Felder CE, Zeev-Ben-Mordehai T, Rydberg EH, Man O, Beckmann JS, Silman I, Sussman JL (2005) FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics 21(16):3435–3438. https://doi.org/10.1093/bioinformatics/bti537

    Article  CAS  PubMed  Google Scholar 

  22. Meszaros B, Erdos G, Dosztanyi Z (2018) IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res 46(W1):W329–W337. https://doi.org/10.1093/nar/gky384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jones DT, Cozzetto D (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31(6):857–863. https://doi.org/10.1093/bioinformatics/btu744

    Article  CAS  PubMed  Google Scholar 

  24. Hanson J, Yang Y, Paliwal K, Zhou Y (2017) Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks. Bioinformatics 33(5):685–692. https://doi.org/10.1093/bioinformatics/btw678

    Article  CAS  PubMed  Google Scholar 

  25. Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z (2006) Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 7:208. https://doi.org/10.1186/1471-2105-7-208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Necci M, Piovesan D, Dosztanyi Z, Tosatto SCE (2017) MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins. Bioinformatics 33(9):1402–1404. https://doi.org/10.1093/bioinformatics/btx015

    Article  CAS  PubMed  Google Scholar 

  27. Kozlowski LP, Bujnicki JM (2012) MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics 13:111. https://doi.org/10.1186/1471-2105-13-111

    Article  PubMed  PubMed Central  Google Scholar 

  28. Anbo H, Amagai H, Fukuchi S (2020) NeProc predicts binding segments in intrinsically disordered regions without learning binding region sequences. Biophys Physicobiol 17:147–154. https://doi.org/10.2142/biophysico.BSJ-2020026

    Article  PubMed  PubMed Central  Google Scholar 

  29. Monastyrskyy B, Kryshtafovych A, Moult J, Tramontano A, Fidelis K (2014) Assessment of protein disorder region predictions in CASP10. Proteins 82(Suppl 2):127–137. https://doi.org/10.1002/prot.24391

    Article  CAS  PubMed  Google Scholar 

  30. Eddy SR (2011) Accelerated profile HMM searches. PLoS Comput Biol 7(10):e1002195. https://doi.org/10.1371/journal.pcbi.1002195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44(D1):D279–D285. https://doi.org/10.1093/nar/gkv1344

    Article  CAS  PubMed  Google Scholar 

  32. Andreeva A, Howorth D, Chothia C, Kulesha E, Murzin AG (2014) SCOP2 prototype: a new approach to protein structure mining. Nucleic Acids Res 42(Database issue):D310–D314. https://doi.org/10.1093/nar/gkt1242

    Article  CAS  PubMed  Google Scholar 

  33. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3):567–580. https://doi.org/10.1006/jmbi.2000.4315

    Article  CAS  PubMed  Google Scholar 

  34. Wootton JC (1994) Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem 18(3):269–285. https://doi.org/10.1016/0097-8485(94)85023-2

    Article  CAS  PubMed  Google Scholar 

  35. Mitrea DM, Kriwacki RW (2016) Phase separation in biology; functional organization of a higher order. Cell Commun Signal 14:1. https://doi.org/10.1186/s12964-015-0125-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chong PA, Forman-Kay JD (2016) Liquid-liquid phase separation in cellular signaling systems. Curr Opin Struct Biol 41:180–186. https://doi.org/10.1016/j.sbi.2016.08.001

    Article  CAS  PubMed  Google Scholar 

  37. Kato M, Han TW, Xie S, Shi K, Du X, Wu LC, Mirzaei H, Goldsmith EJ, Longgood J, Pei J, Grishin NV, Frantz DE, Schneider JW, Chen S, Li L, Sawaya MR, Eisenberg D, Tycko R, McKnight SL (2012) Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149(4):753–767. https://doi.org/10.1016/j.cell.2012.04.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Pak CW, Kosno M, Holehouse AS, Padrick SB, Mittal A, Ali R, Yunus AA, Liu DR, Pappu RV, Rosen MK (2016) Sequence determinants of intracellular phase separation by complex coacervation of a disordered protein. Mol Cell 63(1):72–85. https://doi.org/10.1016/j.molcel.2016.05.042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vernon RM, Chong PA, Tsang B, Kim TH, Bah A, Farber P, Lin H, Forman-Kay JD (2018) Pi-Pi contacts are an overlooked protein feature relevant to phase separation. elife 7. https://doi.org/10.7554/eLife.31486

  40. Feng H, Jenkins LM, Durell SR, Hayashi R, Mazur SJ, Cherry S, Tropea JE, Miller M, Wlodawer A, Appella E, Bai Y (2009) Structural basis for p300 Taz2-p53 TAD1 binding and modulation by phosphorylation. Structure 17(2):202–210. https://doi.org/10.1016/j.str.2008.12.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kriwacki RW, Hengst L, Tennant L, Reed SI, Wright PE (1996) Structural studies of p21Waf1/Cip1/Sdi1 in the free and Cdk2-bound state: conformational disorder mediates binding diversity. Proc Natl Acad Sci U S A 93(21):11504–11509. https://doi.org/10.1073/pnas.93.21.11504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sugase K, Dyson HJ, Wright PE (2007) Mechanism of coupled folding and binding of an intrinsically disordered protein. Nature 447(7147):1021–1025. https://doi.org/10.1038/nature05858

    Article  CAS  PubMed  Google Scholar 

  43. Borgia A, Borgia MB, Bugge K, Kissling VM, Heidarsson PO, Fernandes CB, Sottini A, Soranno A, Buholzer KJ, Nettels D, Kragelund BB, Best RB, Schuler B (2018) Extreme disorder in an ultrahigh-affinity protein complex. Nature 555(7694):61–66. https://doi.org/10.1038/nature25762

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mittag T, Orlicky S, Choy WY, Tang X, Lin H, Sicheri F, Kay LE, Tyers M, Forman-Kay JD (2008) Dynamic equilibrium engagement of a polyvalent ligand with a single-site receptor. Proc Natl Acad Sci U S A 105(46):17772–17777. https://doi.org/10.1073/pnas.0809222105

    Article  PubMed  PubMed Central  Google Scholar 

  45. Demarest SJ, Martinez-Yamout M, Chung J, Chen H, Xu W, Dyson HJ, Evans RM, Wright PE (2002) Mutual synergistic folding in recruitment of CBP/p300 by p160 nuclear receptor coactivators. Nature 415(6871):549–553. https://doi.org/10.1038/415549a

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We are grateful to Prof. Keiichi Homma for his careful reading of this manuscript.

Author information

Authors and Affiliations

  1. Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Japan

    Hiroto Anbo & Satoshi Fukuchi

  2. Graduate School of Information Sciences, Nagoya University, Nagoya, Japan

    Motonori Ota

Authors
  1. Hiroto Anbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Motonori Ota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Satoshi Fukuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Satoshi Fukuchi .

Editor information

Editors and Affiliations

  1. Faculty of Chemistry, University of Warsaw, Warsaw, Poland

    Sławomir Filipek

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Anbo, H., Ota, M., Fukuchi, S. (2023). Computational Methods to Predict Intrinsically Disordered Regions and Functional Regions in Them. In: Filipek, S. (eds) Homology Modeling. Methods in Molecular Biology, vol 2627. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2974-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2974-1_13

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2973-4

  • Online ISBN: 978-1-0716-2974-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation