Abstract
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 http://biomine.cs.vcu.edu/servers/DisoRDPbind/. 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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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):6589–6631
Dunker AK, Obradovic Z (2001) The protein trinity–linking function and disorder. Nat Biotechnol 19(9):805–806
Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293(2):321–331
Uversky VN, Gillespie JR, Fink AL (2000) Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins 41(3):415–427
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):827–839
Walsh I, Martin AJ, Di Domenico T, Tosatto SC (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28(4):503–509
Peng K, Radivojac P, Vucetic S, Dunker AK, Obradovic Z (2006) Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 7:208
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):3069–3090
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):e1259708
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
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):445–452
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):448–464
Meng F, Uversky V, Kurgan L (2017) Computational prediction of intrinsic disorder in proteins. Curr Protoc Protein Sci 88:2 16 11–2 16 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–i496
Jones DT, Cozzetto D (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31(6):857–863
Peng Z, Mizianty MJ, Kurgan L (2014) Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 82(1):145–158
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):137–149
Pancsa R, Tompa P (2012) Structural disorder in eukaryotes. PLoS One 7(4):e34687
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
Tompa P (2012) Intrinsically disordered proteins: a 10-year recap. Trends Biochem Sci 37(12):509–516
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):137–151
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:e1800243
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):1671–1680
Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6(3):197–208
Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z (2002) Intrinsic disorder and protein function. Biochemistry 41(21):6573–6582
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):1882–1898
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):888–898
Cumberworth A, Lamour G, Babu MM, Gsponer J (2013) Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes. Biochem J 454:361–369
Dyson HJ (2012) Roles of intrinsic disorder in protein-nucleic acid interactions. Mol BioSyst 8(1):97–104
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):6806–6843
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):890–901
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):1477–1504
Peng Z, Mizianty MJ, Xue B, Kurgan L, Uversky VN (2012) More than just tails: intrinsic disorder in histone proteins. Mol BioSyst 8(7):1886–1901
Tompa P, Csermely P (2004) The role of structural disorder in the function of RNA and protein chaperones. FASEB J 18(11):1169–1175
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):2561–2569
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):1486–1498
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:e1800064
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):1409–1417
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):1616–1622
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):S3
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):e97725
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–W248
Kumar M, Gromiha MM, Raghava GP (2008) Prediction of RNA binding sites in a protein using SVM and PSSM profile. Proteins 71(1):189–194
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):e0133260
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 13:89
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):e84
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):88–105
Meszaros B, Simon I, Dosztanyi Z (2009) Prediction of protein binding regions in disordered proteins. PLoS Comput Biol 5(5):e1000376
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):e72838
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–i83
Meng F, Kurgan L (2018) High-throughput prediction of disordered moonlighting regions in protein sequences. Proteins 86(10):1097–1110
Meng F, Kurgan L (2016) DFLpred: high-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 32(12):i341–i350
Oldfield CJ, Uversky VN, Kurgan L (2018) Predicting functions of disordered proteins with MoRFpred. Methods Mol Biol 1851:337–352
Yan J, Dunker AK, Uversky VN, Kurgan L (2016) Molecular recognition features (MoRFs) in three domains of life. Mol BioSyst 12(3):697–710
Peng Z, Kurgan L (2015) High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 43(18):e121
Peng Z, Wang C, Uversky VN, Kurgan L (2017) Prediction of disordered RNA, DNA, and protein binding regions using DisoRDPbind. Methods Mol Biol 1484:187–203
Gawlik K, Gallay PA (2014) HCV core protein and virus assembly: what we know without structures. Immunol Res 60(1):1–10
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):712–725
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):3632–3642
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–D227
Wootton JC, Federhen S (1993) Statistics of local complexity in amino-acid-sequences and sequence databases. Comput Chem 17(2):149–163
McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405
Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 27(1):368–369
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
World Health Assembly (2010) Viral hepatitis: report by the secretariat, vol A63/15. World Health Organization, Geneva
Acknowledgments
This research was supported in part by the Robert J. Mattauch Endowment funds and the National Science Foundation grant 1617369 to Lukasz Kurgan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Oldfield, C.J., Peng, Z., Kurgan, L. (2020). Disordered RNA-Binding Region Prediction with DisoRDPbind. In: Heise, T. (eds) RNA Chaperones. Methods in Molecular Biology, vol 2106. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0231-7_14
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0231-7_14
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0230-0
Online ISBN: 978-1-0716-0231-7
eBook Packages: Springer Protocols