Computational Prediction of Intrinsic Disorder in Proteins

Fanchi Meng1, Vladimir Uversky2, Lukasz Kurgan3

1 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, 2 Laboratory of Structural Dynamics, Stability and Folding of Proteins, Institute of Cytology, Russian Academy of Sciences, St. Petersburg, 3 Department of Computer Science, Virginia Commonwealth University, Richmond
Publication Name:  Current Protocols in Protein Science
Unit Number:  Unit 2.16
DOI:  10.1002/cpps.28
Online Posting Date:  April, 2017
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library


Computational prediction of intrinsically disordered proteins (IDPs) is a mature research field. These methods predict disordered residues and regions in an input protein chain. More than 60 predictors of IDPs have been developed. This unit defines computational prediction of intrinsic disorder, summarizes major types of predictors of disorder, and provides details about three accurate and recently released methods. We demonstrate the use of these methods to predict intrinsic disorder for several illustrative proteins, provide insights into how predictions should be interpreted, and quantify and discuss predictive performance. Predictions can be freely and conveniently obtained using webservers. We point to the availability of databases that provide access to annotations of intrinsic disorder determined by structural studies and putative intrinsic disorder pre‐computed by computational methods. Lastly, we also summarize experimental methods that can be used to validate computational predictions. © 2017 by John Wiley & Sons, Inc.

Keywords: intrinsic disorder; intrinsically disordered protein; prediction

PDF or HTML at Wiley Online Library

Table of Contents

  • Introduction
  • Prediction of Intrinsic Disorder from Sequence
  • Selected Computational Predictors of Intrinsic Disorder
  • Assessment of Predictive Performance of Computational Predictors of Intrinsic Disorder
  • Consensus‐Based Predictions
  • Experimental Means for Validation of Predicted Disorder
  • Conclusions
  • Literature Cited
  • Figures
  • Tables
PDF or HTML at Wiley Online Library


PDF or HTML at Wiley Online Library



Literature Cited

  Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., … Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28, 235–242. doi: 10.1093/nar/28.1.235
  Bobst, C. E., & Kaltashov, I. A. (2012). Localizing flexible regions in proteins using hydrogen‐deuterium exchange mass spectrometry. Methods in Molecular Biology, 896, 375–385. doi: 10.1007/978‐1‐4614‐3704‐8_25
  Chandra, V., Huang, P., Hamuro, Y., Raghuram, S., Wang, Y., Burris, T. P., & Rastinejad, F. (2008). Structure of the intact PPAR‐gamma‐RXR‐ nuclear receptor complex on DNA. Nature, 456, 350–356. doi: 10.1038/nature07413
  Consortium, T. U. (2010). The universal protein resource (UniProt) in 2010. Nucleic Acids Research, 38, D142–D148. doi: 10.1093/nar/gkp846
  Daughdrill, G. W., Pielak, G. J., Uversky, V. N., Cortese, M. S., & Dunker, A. K. (2005). Natively disordered proteins. In J. Buchner & T. Kiefhaber (Eds.), Handbook of protein folding (pp. 271–353). Weinheim, Germany: Wiley‐VCH, Verlag GmbH & Co. KGaA.
  Deng, X., Eickholt, J., & Cheng, J. (2012). A comprehensive overview of computational protein disorder prediction methods. Molecular BioSystems, 8, 114–121. doi: 10.1039/C1MB05207A
  Di Domenico, T., Walsh, I., Martin, A. J. M., & Tosatto, S. C. E. (2012). MobiDB: A comprehensive database of intrinsic protein disorder annotations. Bioinformatics, 28, 2080–2081. doi: 10.1093/bioinformatics/bts327
  Disfani, F. M., Hsu, W. L., Mizianty, M. J., Oldfield, C. J., Xue, B., Dunker, A. K., … 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, i75–83. doi: 10.1093/bioinformatics/bts209
  Dosztányi, Z., Csizmok, V., Tompa, P., & Simon, I. (2005a). IUPred: Web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics, 21, 3433–3434. doi: 10.1093/bioinformatics/bti541
  Dosztányi, Z., Csizmók, V., Tompa, P., & Simon, I. (2005b). The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. Journal of Molecular Biology, 347, 827–839. doi: 10.1016/j.jmb.2005.01.071
  Dosztanyi, Z., Meszaros, B., & Simon, I. (2009). ANCHOR: Web server for predicting protein binding regions in disordered proteins. Bioinformatics, 25, 2745–2746. doi: 10.1093/bioinformatics/btp518
  Dunker, A. K., Obradovic, Z., Romero, P., Garner, E. C., & Brown, C. J. (2000). Intrinsic protein disorder in complete genomes. Genome Informatics. Workshop on Genome Informatics, 11, 161–171.
  Dunker, A. K., Babu, M. M., Barbar, E., Blackledge, M., Bondos, S. E., Dosztányi, Z., … Uversky, V. N. (2013). What's in a name? Why these proteins are intrinsically disordered. Intrinsically Disordered Proteins, 1, e24157. doi: 10.4161/idp.24157
  Dunker, A. K., Cortese, M. S., Romero, P., Iakoucheva, L. M., & Uversky, V. N. (2005). Flexible nets. the roles of intrinsic disorder in protein interaction networks. The FEBS Journal, 272, 5129–5148. doi: 10.1111/j.1742‐4658.2005.04948.x
  Dyson, H. J., & Wright, P. E. (2005). Intrinsically unstructured proteins and their functions. Nature Reviews Molecular Cell Biology, 6, 197–208. doi: 10.1038/nrm1589
  Eliezer, D. (2009). Biophysical characterization of intrinsically disordered proteins. Current Opinion in Structural Biology, 19, 23–30. doi: 10.1016/
  Fan, X., & Kurgan, L. (2014). Accurate prediction of disorder in protein chains with a comprehensive and empirically designed consensus. Journal of Biomolecular Structure & Dynamics, 32, 448–464. doi: 10.1080/07391102.2013.775969
  Fontana, A., de Laureto, P. P., Spolaore, B., & Frare, E. (2012). Identifying disordered regions in proteins by limited proteolysis. Methods in Molecular Biology, 896, 297–318. doi: 10.1007/978‐1‐4614‐3704‐8_20
  Fontana, A., de Laureto, P. P., Spolaore, B., Frare, E., Picotti, P., & Zambonin, M. (2004). Probing protein structure by limited proteolysis. Acta Biochimica Polonica, 51, 299–321.
  Fukuchi, S., Amemiya, T., Sakamoto, S., Nobe, Y., Hosoda, K., Kado, Y., … Ota, M. (2014). IDEAL in 2014 illustrates interaction networks composed of intrinsically disordered proteins and their binding partners. Nucleic Acids Research, 42, D320–325. doi: 10.1093/nar/gkt1010
  Fukuchi, S., Sakamoto, S., Nobe, Y., Murakami, S. D., Amemiya, T., Hosoda, K., … Ota, M. (2012). IDEAL: Intrinsically disordered proteins with extensive annotations and literature. Nucleic Acids Research, 40, D507–D511. doi: 10.1093/nar/gkr884
  Fuxreiter, M., Tompa, P., Simon, I., Uversky, V. N., Hansen, J. C., & Asturias, F. J. (2008). Malleable machines take shape in eukaryotic transcriptional regulation. Nature Chemical Biology, 4, 728–737. doi: 10.1038/nchembio.127
  Fuxreiter, M., Toth‐Petroczy, A., Kraut, D. A., Matouschek, A., Lim, R. Y., Xue, B., … Uversky, V. N. (2014). Disordered proteinaceous machines. Chemical Reviews, 114, 6806–6843. doi: 10.1021/cr4007329
  Galea, C. A., Wang, Y., Sivakolundu, S. G., & Kriwacki, R. W. (2008). Regulation of cell division by intrinsically unstructured proteins: Intrinsic flexibility, modularity, and signaling conduits. Biochemistry, 47, 7598–7609. doi: 10.1021/bi8006803
  Hu, G., Wu, Z., Wang, K., Uversky, V. N., & Kurgan, L. (2015). Untapped potential of disordered proteins in current druggable human proteome. Current Drug Targets, 17, 1198–1205. doi: 10.2174/1389450116666150722141119
  Ishida, T., & Kinoshita, K. (2007). PrDOS: Prediction of disordered protein regions from amino acid sequence. Nucleic Acids Research, 35, W460–W464. doi: 10.1093/nar/gkm363
  Jensen, M. R., Salmon, L., Nodet, G., & Blackledge, M. (2010). Defining conformational ensembles of intrinsically disordered and partially folded proteins directly from chemical shifts. Journal of the American Chemical Society, 132, 1270–1272. doi: 10.1021/ja909973n
  Jones, D. T., & Cozzetto, D. (2015). DISOPRED3: Precise disordered region predictions with annotated protein‐binding activity. Bioinformatics, 31, 857–863. doi: 10.1093/bioinformatics/btu744
  Jones, D. T., & Ward, J. J. (2003). Prediction of disordered regions in proteins from position specific score matrices. Proteins: Structure Function and Bioinformatics, 53, 573–578. doi: 10.1002/prot.10528
  Kozlowski, L. P., & Bujnicki, J. M. (2012). MetaDisorder: A meta‐server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics, 13, 1–11. doi: 10.1186/1471‐2105‐13‐111
  Le Gall, T., Romero, P. R., Cortese, M. S., Uversky, V. N., & Dunker, A. K. (2007). Intrinsic disorder in the protein data bank. Journal of Biomolecular Structure & Dynamics, 24, 325–342. doi: 10.1080/07391102.2007.10507123
  Li, X., Romero, P., Rani, M., Dunker, A. K., & Obradovic, Z. (1999). Predicting protein disorder for N‐, C‐, and internal regions. Genome Informatics. Workshop on Genome Informatics, 10, 30–40.
  Linding, R., Jensen, L. J., Diella, F., Bork, P., Gibson, T. J., & Russell, R. B. (2003a). Protein disorder prediction: Implications for structural proteomics. Structure, 11, 1453–1459. doi: 10.1016/j.str.2003.10.002
  Linding, R., Russell, R. B., Neduva, V., & Gibson, T. J. (2003b). GlobPlot: Exploring protein sequences for globularity and disorder. Nucleic Acids Research, 31, 3701–3708. doi: 10.1093/nar/gkg519
  Liu, J., Perumal, N. B., Oldfield, C. J., Su, E. W., Uversky, V. N., & Dunker, A. K. (2006). Intrinsic disorder in transcription factors. Biochemistry, 45, 6873–6888. doi: 10.1021/bi0602718
  Liu, J., & Rost, B. (2003). NORSp: Predictions of long regions without regular secondary structure. Nucleic Acids Research, 31, 3833–3835. doi: 10.1093/nar/gkg515
  Malhis, N., Jacobson, M., & Gsponer, J. (2016). MoRFchibi SYSTEM: Software tools for the identification of MoRFs in protein sequences. Nucleic Acids Research, 44, W488–W493. doi: 10.1093/nar/gkw409
  Martin, A. J. M., Walsh, I., & Tosatto, S. C. E. (2010). MOBI: A web server to define and visualize structural mobility in NMR protein ensembles. Bioinformatics, 26, 2916–2917. doi: 10.1093/bioinformatics/btq537
  McGuffin, L. J., Atkins, J. D., Salehe, B. R., Shuid, A. N., & Roche, D. B. (2015). IntFOLD: An integrated server for modelling protein structures and functions from amino acid sequences. Nucleic Acids Research, 43, W169–W173. doi: 10.1093/nar/gkv236
  Meng, F., & Kurgan, L. (2016). DFLpred: High‐throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics, 32, i341–i350. doi: 10.1093/bioinformatics/btw280
  Mizianty, M. J., Peng, Z. L., & Kurgan, L. (2013). MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles. Intrinsically Disordered Proteins, 1, e24428. doi: 10.4161/idp.24428
  Mizianty, M. J., Stach, W., Chen, K., Kedarisetti, K. D., Disfani, F. M., & Kurgan, L. (2010). Improved sequence‐based prediction of disordered regions with multilayer fusion of multiple information sources. Bioinformatics, 26, i489–i496. doi: 10.1093/bioinformatics/btq373
  Mizianty, M. J., Uversky, V., & Kurgan, L. (2014). Prediction of intrinsic disorder in proteins using MFDp2. Methods in Molecular Biology, 1137, 147–162. doi: 10.1007/978‐1‐4939‐0366‐5_11
  Mizianty, M. J., Zhang, T., Xue, B., Zhou, Y., Dunker, A. K., Uversky, V. N., & Kurgan, L. (2011). In‐silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics, 12, 245. doi: 10.1186/1471‐2105‐12‐245
  Monastyrskyy, B., Kryshtafovych, A., Moult, J., Tramontano, A., & Fidelis, K. (2014). Assessment of protein disorder region predictions in CASP10. Proteins, 82, 127–137. doi: 10.1002/prot.24391
  Nodet, G., Salmon, L., Ozenne, V., Meier, S., Jensen, M. R., & Blackledge, M. (2009). Quantitative description of backbone conformational sampling of unfolded proteins at amino acid resolution from NMR residual dipolar couplings. Journal of the American Chemical Society, 131, 17908–17918. doi: 10.1021/ja9069024
  Oates, M. E., Romero, P., Ishida, T., Ghalwash, M., Mizianty, M. J., Xue, B., … Gough, J. (2013). D(2)P(2): Database of disordered protein predictions. Nucleic Acids Research, 41, D508–516. doi: 10.1093/nar/gks1226
  Obradovic, Z., Peng, K., Vucetic, S., Radivojac, P., & Dunker, A. K. (2005). Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins, 61(Suppl 7), 176–182. doi: 10.1002/prot.20735
  Obradovic, Z., Peng, K., Vucetic, S., Radivojac, P., Brown, C. J., & Dunker, A. K. (2003). Predicting intrinsic disorder from amino acid sequence. Proteins, 53(Suppl 6), 566–572. doi: 10.1002/prot.10532
  Peng, Z., & Kurgan, L. (2012a). On the complementarity of the consensus‐based disorder prediction. Pacific Symposium on Biocomputing, 176–187.
  Peng, Z. L., & Kurgan, L. (2012b). Comprehensive comparative assessment of in‐silico predictors of disordered regions. Current Protein & Peptide Science, 13, 6–18. doi: 10.2174/138920312799277938
  Peng, Z., & Kurgan, L. (2015). High‐throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Research, 43, e121. doi: 10.1093/nar/gkv585
  Peng, Z., Oldfield, C. J., Xue, B., Mizianty, M. J., Dunker, A. K., Kurgan, L., & Uversky, V. N. (2014). A creature with a hundred waggly tails: Intrinsically disordered proteins in the ribosome. Cellular and Molecular Life Sciences, 71, 1477–1504. doi: 10.1007/s00018‐013‐1446‐6
  Peng, Z., Wang, C., Uversky, A. V., & Kurgan, L. (2015a). Prediction of disordered RNA, DNA, and protein binding regions using DisoRDPbind. Methods in Molecular Biology, 1484, 187–203. doi: 10.1007/978‐1‐4939‐6406‐2_14
  Peng, Z., Yan, J., Fan, X., Mizianty, M. J., Xue, B., Wang, K., … Kurgan, L. (2015b). Exceptionally abundant exceptions: Comprehensive characterization of intrinsic disorder in all domains of life. Cellular and Molecular Life Sciences, 72, 137–151. doi: 10.1007/s00018‐014‐1661‐9
  Pentony, M., Ward, J., & Jones, D. (2010). Computational resources for the prediction and analysis of native disorder in proteins. In S. J. Hubbard & A. R. Jones (Eds.), Proteome Bioinformatics (Vol. 604, pp. 369–393). Humana Press.
  Piovesan, D., Tabaro, F., Mičetić, I., Necci, M., Quaglia, F., Oldfield, C. J., … Tosatto, S. C. (2017). DisProt 7.0: A major update of the database of disordered proteins. Nucleic Acids Research, 45, D1123–D1124. doi: 10.1093/nar/gkw1279
  Potenza, E., Domenico, T. D., Walsh, I., & Tosatto, S. C. E. (2015). MobiDB 2.0: An improved database of intrinsically disordered and mobile proteins. Nucleic Acids Research, 43, D315–D320. doi: 10.1093/nar/gku982
  Radivojac, P., Obradovic, Z., Smith, D. K., Zhu, G., Vucetic, S., Brown, C. J., … Dunker, A. K. (2004). Protein flexibility and intrinsic disorder. Protein Science, 13, 71–80. doi: 10.1110/ps.03128904
  Receveur‐Brechot, V., Bourhis, J. M., Uversky, V. N., Canard, B., & Longhi, S. (2006). Assessing protein disorder and induced folding. Proteins, 62, 24–45. doi: 10.1002/prot.20750
  Romero, P., Obradovic, Z., Kissinger, C., Villafranca, J. E., & Dunker, A. K. (1997). Identifying disordered regions in proteins from amino acid sequence. In IEEE International Conference on Neural Networks – Conference Proceedings. (Vol. 1, pp. 90‐95). IEEE.
  Romero, P., Obradovic, Z., Li, X., Garner, E. C., Brown, C. J., & Dunker, A. K. (2001). Sequence complexity of disordered protein. Proteins: Structure Function, and Bioinformatics, 42, 38–48. doi: 10.1002/1097‐0134(20010101)42:1%3c38::AID‐PROT50%3e3.0.CO;2‐3
  Salmon, L., Nodet, G., Ozenne, V., Yin, G., Jensen, M. R., Zweckstetter, M., & Blackledge, M. (2010). NMR characterization of long‐range order in intrinsically disordered proteins. Journal of the American Chemical Society, 132, 8407–8418. doi: 10.1021/ja101645g
  Sickmeier, M., Hamilton, J. A., LeGall, T., Vacic, V., Cortese, M. S., Tantos, A., … Dunker, A. K. (2007). DisProt: The database of disordered proteins. Nucleic Acids Research, 35, D786–D793. doi: 10.1093/nar/gkl893
  Smith, D. L., Deng, Y., & Zhang, Z. (1997). Probing the non‐covalent structure of proteins by amide hydrogen exchange and mass spectrometry. Journal of Mass Spectrometry, 32, 135–146. doi: 10.1002/(SICI)1096‐9888(199702)32:2%3c135::AID‐JMS486%3e3.0.CO;2‐M
  Tompa, P. (2002). Intrinsically unstructured proteins. Trends in Biochemical Sciences, 27, 527–533. doi: 10.1016/S0968‐0004(02)02169‐2
  Uversky, V. N. (2015). Biophysical methods to investigate intrinsically disordered proteins: Avoiding an “Elephant and Blind Men” situation. Advances in Experimental Medicine and Biology, 870, 215–260. doi: 10.1007/978‐3‐319‐20164‐1_7
  Uversky, V. N., & Dunker, A. K. (Eds.). (2012a). Intrinsically disordered protein analysis: Volume I. Methods and Experimental Tools (Vol. 895). Totowa, NJ: Humana Press.
  Uversky, V. N., & Dunker, A. K. (Eds.). (2012b). Intrinsically disordered protein analysis: Volume II. Methods and Experimental Tools. Totowa, NJ: Humana Press.
  Uversky, V. N., & Dunker, A. K. (2012c). Multiparametric analysis of intrinsically disordered proteins: Looking at intrinsic disorder through compound eyes. Analytical Chemistry, 84, 2096–2104. doi: 10.1021/ac203096k
  Uversky, V. N., & Longhi, S. (2010). Instrumental analysis of intrinsically disordered proteins: Assessing structure and conformation. New Jersey, USA: John Wiley & Sons.
  Uversky, V. N., Oldfield, C. J., & Dunker, A. K. (2005). Showing your ID: Intrinsic disorder as an ID for recognition, regulation and cell signaling. Journal of Molecular Recognition, 18, 343–384. doi: 10.1002/jmr.747
  Uversky, V. N., Oldfield, C. J., & Dunker, A. K. (2008). Intrinsically disordered proteins in human diseases: Introducing the D2 concept. Annual Review of Biophysics and Bioengineering, 37, 215–246. doi: 10.1146/annurev.biophys.37.032807.125924
  van der Lee, R., Buljan, M., Lang, B., Weatheritt, R. J., Daughdrill, G. W., Dunker, A. K., … Babu, M. M. (2014). Classification of intrinsically disordered regions and proteins. Chemical Reviews, 114, 6589–6631. doi: 10.1021/cr400525m
  Varadi, M., Zsolyomi, F., Guharoy, M., & Tompa, P. (2015). Functional advantages of conserved intrinsic disorder in RNA‐Binding proteins. PLoS One, 10, e0139731. doi: 10.1371/journal.pone.0139731
  Wang, C., Uversky, V. N., & 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, 1486–1498. doi: 10.1002/pmic.201500177
  Ward, J. J., McGuffin, L. J., Bryson, K., Buxton, B. F., & Jones, D. T. (2004a). The DISOPRED server for the prediction of protein disorder. Bioinformatics, 20, 2138–2139. doi: 10.1093/bioinformatics/bth195
  Ward, J. J., Sodhi, J. S., McGuffin, L. J., Buxton, B. F., & Jones, D. T. (2004b). Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. Journal of Molecular Biology, 337, 635–645. doi: 10.1016/j.jmb.2004.02.002
  Xie, H., Vucetic, S., Iakoucheva, L. M., Oldfield, C. J., Dunker, A. K., Uversky, V. N., & Obradovic, Z. (2007). Functional anthology of intrinsic disorder. 1. biological processes and functions of proteins with long disordered regions. Journal of Proteome Research, 6, 1882–1898. doi: 10.1021/pr060392u
  Xue, B., Dunbrack, R. L., Williams, R. W., Dunker, A. K., & Uversky, V. N. (2010). PONDR‐FIT: A meta‐predictor of intrinsically disordered amino acids. Biochimica et Biophysica Acta, 1804, 996–1010. doi: 10.1016/j.bbapap.2010.01.011
  Xue, B., Dunker, A. K., & Uversky, V. N. (2012). Orderly order in protein intrinsic disorder distribution: Disorder in 3500 proteomes from viruses and the three domains of life. Journal of Biomolecular Structure & Dynamics, 30, 137–149. doi: 10.1080/07391102.2012.675145
  Yan, J., Dunker, A. K., Uversky, V. N., & Kurgan, L. (2015). Molecular recognition features (MoRFs) in three domains of life. Molecular Biosystems, 12, 697–710. doi: 10.1039/c5mb00640f
Key References
  Ishida & Kinoshita, 2007. See above.
  Describes PrDOS, one of the most accurate hybrid method for the prediction of intrinsic disorder.
  Jones & Cozzetto, 2015. See above.
  Describes DISOPRED3, one of the most accurate machine learning method for the prediction of intrinsic disorder and disordered protein binding regions.
  Mizianty et al., 2010. See above.
  Describes MFDp, one of the most accurate meta method for the prediction of intrinsic disorder.
  Peng & Kurgan, 2012b. See above.
  Provides comprehensive empirical assessment of predictive performance of modern methods for the prediction of intrinsic disorder.
  Piovesan et al., 2017. See above.
  Introduces and describes the DisProt database of the intrinsically disordered proteins.
  van der Lee et al., 2014. See above.
  Defines intrinsic disorder and discusses the relevant experimental and computational tools.
Internet Resources
  DISOPRED3's webserver.
  MFDp's webserver.
  D2P2 database.
  MobiDB database.‐bin/top.cgi
  PrDOS's webserver.
PDF or HTML at Wiley Online Library