RNA Chaperones pp 225-239 | Cite as

Disordered RNA-Binding Region Prediction with DisoRDPbind

  • Christopher J. Oldfield
  • Zhenling Peng
  • Lukasz KurganEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2106)


RNA chaperone activity is one of the many functions of intrinsically disordered regions (IDRs). IDRs function without the prerequisite of a stable structure. Instead, their functions arise from structural ensembles. A common theme in IDR function is molecular recognition; IDRs mediate interactions with other proteins, RNA, and DNA. Many computational methods are available to predict IDRs from protein sequence, but relatively few are available for predicting IDR functions. Available methods primarily focus on protein-protein interactions. DisoRDPbind was developed to predict several protein functions including interactions with RNA. This method is available as a user-friendly web interface, located at The development and architecture of DisoRDPbind is briefly presented, and its accuracy relative to other RNA-binding residue predictors is discussed. We explain usage of the web interface in detail and provide an example of prediction results and interpretation. While DisoRDPbind does not identify RNA chaperones directly, we provide a case study of an RNA chaperone, HCV core protein, as an example of the method’s utility in the study of RNA chaperones.

Key words

Intrinsic disorder Protein-RNA interactions Intrinsically disordered regions Molecular recognition 



This research was supported in part by the Robert J. Mattauch Endowment funds and the National Science Foundation grant 1617369 to Lukasz Kurgan.


  1. 1.
    van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones DT, Kim PM, Kriwacki RW, Oldfield CJ, Pappu RV, Tompa P, Uversky VN, Wright PE, Babu MM (2014) Classification of intrinsically disordered regions and proteins. Chem Rev 114(13):6589–6631PubMedPubMedCentralGoogle Scholar
  2. 2.
    Dunker AK, Obradovic Z (2001) The protein trinity–linking function and disorder. Nat Biotechnol 19(9):805–806PubMedGoogle Scholar
  3. 3.
    Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293(2):321–331PubMedGoogle Scholar
  4. 4.
    Uversky VN, Gillespie JR, Fink AL (2000) Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins 41(3):415–427PubMedGoogle Scholar
  5. 5.
    Dosztanyi Z, Csizmok V, Tompa P, Simon I (2005) The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J Mol Biol 347(4):827–839PubMedGoogle Scholar
  6. 6.
    Walsh I, Martin AJ, Di Domenico T, Tosatto SC (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28(4):503–509PubMedGoogle Scholar
  7. 7.
    Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z (2006) Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 7:208PubMedPubMedCentralGoogle Scholar
  8. 8.
    Meng F, Uversky VN, Kurgan L (2017) Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell Mol Life Sci 74(17):3069–3090PubMedGoogle Scholar
  9. 9.
    Lieutaud P, Ferron F, Uversky AV, Kurgan L, Uversky VN, Longhi S (2016) How disordered is my protein and what is its disorder for? A guide through the “dark side” of the protein universe. Intrinsically Disord Proteins 4(1):e1259708PubMedPubMedCentralGoogle Scholar
  10. 10.
    Monastyrskyy B, Kryshtafovych A, Moult J, Tramontano A, Fidelis K (2014) Assessment of protein disorder region predictions in CASP10. Proteins 82(Suppl 2):127–137PubMedGoogle Scholar
  11. 11.
    Necci M, Piovesan D, Dosztanyi Z, Tompa P, Tosatto SCE (2017) A comprehensive assessment of long intrinsic protein disorder from the DisProt database. Bioinformatics 34(3):445–452Google Scholar
  12. 12.
    Fan X, Kurgan L (2014) Accurate prediction of disorder in protein chains with a comprehensive and empirically designed consensus. J Biomol Struct Dyn 32(3):448–464PubMedGoogle Scholar
  13. 13.
    Meng F, Uversky V, Kurgan L (2017) Computational prediction of intrinsic disorder in proteins. Curr Protoc Protein Sci 88:2 16 11–2 16 14Google Scholar
  14. 14.
    Mizianty MJ, Stach W, Chen K, Kedarisetti KD, Disfani FM, Kurgan L (2010) Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources. Bioinformatics 26(18):i489–i496PubMedPubMedCentralGoogle Scholar
  15. 15.
    Jones DT, Cozzetto D (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31(6):857–863Google Scholar
  16. 16.
    Peng Z, Mizianty MJ, Kurgan L (2014) Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 82(1):145–158PubMedGoogle Scholar
  17. 17.
    Xue B, Dunker AK, Uversky VN (2012) Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life. J Biomol Struct Dyn 30(2):137–149PubMedGoogle Scholar
  18. 18.
    Pancsa R, Tompa P (2012) Structural disorder in eukaryotes. PLoS One 7(4):e34687PubMedPubMedCentralGoogle Scholar
  19. 19.
    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):635–645PubMedGoogle Scholar
  20. 20.
    Tompa P (2012) Intrinsically disordered proteins: a 10-year recap. Trends Biochem Sci 37(12):509–516PubMedGoogle Scholar
  21. 21.
    Peng Z, Yan J, Fan X, Mizianty MJ, Xue B, Wang K, Hu G, Uversky VN, Kurgan L (2015) Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life. Cell Mol Life Sci 72(1):137–151PubMedGoogle Scholar
  22. 22.
    Hu G, Wang K, Song J, Uversky VN, Kurgan L (2018) Taxonomic landscape of the dark proteomes: whole-proteome scale interplay between structural darkness, intrinsic disorder, and crystallization propensity. Proteomics 18:e1800243PubMedGoogle Scholar
  23. 23.
    Yan J, Mizianty MJ, Filipow PL, Uversky VN, Kurgan L (2013) RAPID: fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale. Biochim Biophys Acta 1834(8):1671–1680PubMedGoogle Scholar
  24. 24.
    Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6(3):197–208PubMedGoogle Scholar
  25. 25.
    Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z (2002) Intrinsic disorder and protein function. Biochemistry 41(21):6573–6582PubMedGoogle Scholar
  26. 26.
    Xie H, Vucetic S, Iakoucheva LM, Oldfield CJ, Dunker AK, Uversky VN, Obradovic Z (2007) Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions. J Proteome Res 6(5):1882–1898PubMedPubMedCentralGoogle Scholar
  27. 27.
    Chen JW, Romero P, Uversky VN, Dunker AK (2006) Conservation of intrinsic disorder in protein domains and families: II. Functions of conserved disorder. J Proteome Res 5(4):888–898PubMedPubMedCentralGoogle Scholar
  28. 28.
    Cumberworth A, Lamour G, Babu MM, Gsponer J (2013) Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes. Biochem J 454:361–369PubMedGoogle Scholar
  29. 29.
    Dyson HJ (2012) Roles of intrinsic disorder in protein-nucleic acid interactions. Mol BioSyst 8(1):97–104PubMedGoogle Scholar
  30. 30.
    Fuxreiter M, Toth-Petroczy A, Kraut DA, Matouschek AT, Lim RYH, Xue B, Kurgan L, Uversky VN (2014) Disordered proteinaceous machines. Chem Rev 114(13):6806–6843PubMedPubMedCentralGoogle Scholar
  31. 31.
    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):890–901Google Scholar
  32. 32.
    Peng Z, Oldfield CJ, Xue B, Mizianty MJ, Dunker AK, Kurgan L, Uversky VN (2014) A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome. Cell Mol Life Sci 71(8):1477–1504PubMedGoogle Scholar
  33. 33.
    Peng Z, Mizianty MJ, Xue B, Kurgan L, Uversky VN (2012) More than just tails: intrinsic disorder in histone proteins. Mol BioSyst 8(7):1886–1901PubMedGoogle Scholar
  34. 34.
    Tompa P, Csermely P (2004) The role of structural disorder in the function of RNA and protein chaperones. FASEB J 18(11):1169–1175PubMedGoogle Scholar
  35. 35.
    Wu Z, Hu G, Yang J, Peng Z, Uversky VN, Kurgan L (2015) In various protein complexes, disordered protomers have large per-residue surface areas and area of protein-, DNA- and RNA-binding interfaces. FEBS Lett 589(19 Pt A):2561–2569PubMedGoogle Scholar
  36. 36.
    Wang C, Uversky VN, Kurgan L (2016) Disordered nucleiome: abundance of intrinsic disorder in the DNA- and RNA-binding proteins in 1121 species from Eukaryota, bacteria and Archaea. Proteomics 16(10):1486–1498PubMedGoogle Scholar
  37. 37.
    Chowdhury S, Zhang J, Kurgan L (2018) In silico prediction and validation of novel RNA binding proteins and residues in the human proteome. Proteomics 18:e1800064PubMedGoogle Scholar
  38. 38.
    Ivanyi-Nagy R, Davidovic L, Khandjian EW, Darlix J-L (2005) Disordered RNA chaperone proteins: from functions to disease. Cell Mol Life Sci 62(13):1409–1417PubMedGoogle Scholar
  39. 39.
    Liu ZP, Wu LY, Wang Y, Zhang XS, Chen LN (2010) Prediction of protein-RNA binding sites by a random forest method with combined features. Bioinformatics 26(13):1616–1622PubMedGoogle Scholar
  40. 40.
    Wang L, Huang C, Yang MQ, Yang JY (2010) BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Syst Biol 4(1):S3PubMedPubMedCentralGoogle Scholar
  41. 41.
    Walia RR, Xue LC, Wilkins K, El-Manzalawy Y, Dobbs D, Honavar V (2014) RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins. PLoS One 9(5):e97725PubMedPubMedCentralGoogle Scholar
  42. 42.
    Wang L, Brown SJ (2006) BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Res 34(Web Server):W243–W248PubMedPubMedCentralGoogle Scholar
  43. 43.
    Kumar M, Gromiha MM, Raghava GP (2008) Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins 71(1):189–194PubMedGoogle Scholar
  44. 44.
    Yang X, Wang J, Sun J, Liu R (2015) SNBRFinder: a sequence-based hybrid algorithm for enhanced prediction of nucleic acid-binding residues. PLoS One 10(7):e0133260PubMedPubMedCentralGoogle Scholar
  45. 45.
    Walia RR, Caragea C, Lewis BA, Towfic F, Terribilini M, El-Manzalawy Y, Dobbs D, Honavar V (2012) Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art. BMC Bioinformatics 13:89PubMedPubMedCentralGoogle Scholar
  46. 46.
    Yan J, Kurgan L (2017) DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues. Nucleic Acids Res 45(10):e84PubMedPubMedCentralGoogle Scholar
  47. 47.
    Yan J, Friedrich S, Kurgan L (2016) A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues. Brief Bioinform 17(1):88–105PubMedGoogle Scholar
  48. 48.
    Meszaros B, Simon I, Dosztanyi Z (2009) Prediction of protein binding regions in disordered proteins. PLoS Comput Biol 5(5):e1000376PubMedPubMedCentralGoogle Scholar
  49. 49.
    Khan W, Duffy F, Pollastri G, Shields DC, Mooney C (2013) Predicting binding within disordered protein regions to structurally characterised peptide-binding domains. PLoS One 8(9):e72838PubMedPubMedCentralGoogle Scholar
  50. 50.
    Disfani FM, Hsu WL, Mizianty MJ, Oldfield CJ, Xue B, Dunker AK, Uversky VN, Kurgan L (2012) MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. Bioinformatics 28(12):i75–i83PubMedPubMedCentralGoogle Scholar
  51. 51.
    Meng F, Kurgan L (2018) High-throughput prediction of disordered moonlighting regions in protein sequences. Proteins 86(10):1097–1110PubMedGoogle Scholar
  52. 52.
    Meng F, Kurgan L (2016) DFLpred: high-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 32(12):i341–i350PubMedPubMedCentralGoogle Scholar
  53. 53.
    Oldfield CJ, Uversky VN, Kurgan L (2018) Predicting functions of disordered proteins with MoRFpred. Methods Mol Biol 1851:337–352Google Scholar
  54. 54.
    Yan J, Dunker AK, Uversky VN, Kurgan L (2016) Molecular recognition features (MoRFs) in three domains of life. Mol BioSyst 12(3):697–710PubMedGoogle Scholar
  55. 55.
    Peng Z, Kurgan L (2015) High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 43(18):e121PubMedPubMedCentralGoogle Scholar
  56. 56.
    Peng Z, Wang C, Uversky VN, Kurgan L (2017) Prediction of disordered RNA, DNA, and protein binding regions using DisoRDPbind. Methods Mol Biol 1484:187–203PubMedGoogle Scholar
  57. 57.
    Gawlik K, Gallay PA (2014) HCV core protein and virus assembly: what we know without structures. Immunol Res 60(1):1–10PubMedPubMedCentralGoogle Scholar
  58. 58.
    Ivanyi-Nagy R, Lavergne J-P, Gabus C, Ficheux D, Darlix J-L (2008) RNA chaperoning and intrinsic disorder in the core proteins of Flaviviridae. Nucleic Acids Res 36(3):712–725PubMedGoogle Scholar
  59. 59.
    Sharma K, Didier P, Darlix JL, de Rocquigny H, Bensikaddour H, Lavergne JP, Penin F, Lessinger JM, Mely Y (2010) Kinetic analysis of the nucleic acid chaperone activity of the hepatitis C virus core protein. Nucleic Acids Res 38(11):3632–3642PubMedPubMedCentralGoogle Scholar
  60. 60.
    Piovesan D, Tabaro F, Mičetić I, Necci M, Quaglia F, Oldfield CJ, Aspromonte MC, Davey NE, Davidović R, Dosztányi Z, Elofsson A, Gasparini A, Hatos A, Kajava AV, Kalmar L, Leonardi E, Lazar T, Macedo-Ribeiro S, Macossay-Castillo M, Meszaros A, Minervini G, Murvai N, Pujols J, Roche DB, Salladini E, Schad E, Schramm A, Szabo B, Tantos A, Tonello F, Tsirigos KD, Veljković N, Ventura S, Vranken W, Warholm P, Uversky VN, Dunker AK, Longhi S, Tompa P, Tosatto SCE (2017) DisProt 7.0: a major update of the database of disordered proteins. Nucleic Acids Res 45(Database issue):D219–D227PubMedGoogle Scholar
  61. 61.
    Wootton JC, Federhen S (1993) Statistics of local complexity in amino-acid-sequences and sequence databases. Comput Chem 17(2):149–163Google Scholar
  62. 62.
    McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405PubMedGoogle Scholar
  63. 63.
    Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 27(1):368–369PubMedPubMedCentralGoogle Scholar
  64. 64.
    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):3389–3402PubMedPubMedCentralGoogle Scholar
  65. 65.
    World Health Assembly (2010) Viral hepatitis: report by the secretariat, vol A63/15. World Health Organization, GenevaGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Christopher J. Oldfield
    • 1
  • Zhenling Peng
    • 2
  • Lukasz Kurgan
    • 1
    Email author
  1. 1.Department of Computer ScienceVirginia Commonwealth UniversityRichmondUSA
  2. 2.Center for Applied MathematicsTianjin UniversityTianjinPeople’s Republic of China

Personalised recommendations