Skip to main content
SpringerLink
Log in

Predicting Conformational Disorder

  • Protocol
  • First Online:
Data Mining Techniques for the Life Sciences

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1415))

Abstract

In the last two decades, it has become increasingly evident that a large number of proteins are either fully or partially disordered. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded at the amino acid sequence level, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting disorder and identifying regions involved in induced folding.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. 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

    Article  CAS  PubMed  Google Scholar 

  2. Bogatyreva NS, Finkelstein AV, Galzitskaya OV (2006) Trend of amino acid composition of proteins of different taxa. J Bioinform Comput Biol 4(2):597–608

    Article  CAS  PubMed  Google Scholar 

  3. Dunker AK, Babu MM, Barbar E, Blackledge M, Bondos SE, Dosztányi Z, Dyson HJ, Forman-Kay J, Fuxreiter M, Gsponer J, Han K-H, Jones DT, Longhi S, Metallo SJ, Nishikawa K, Nussinov R, Obradovic Z, Pappu RV, Rost B, Selenko P, Subramaniam V, Sussman JL, Tompa P, Uversky VN (2013) What’s in a name? Why these proteins are intrinsically disordered. Intrinsically Disord Proteins 1:e24157

    Article  Google Scholar 

  4. Uversky VN (2015) The multifaceted roles of intrinsic disorder in protein complexes. FEBS Lett. doi:10.1016/j.febslet.2015.06.004

    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

    Article  PubMed  PubMed Central  Google Scholar 

  6. Habchi J, Tompa P, Longhi S, Uversky VN (2014) Introducing protein intrinsic disorder. Chem Rev 114(13):6561–6588. doi:10.1021/cr400514h

    Article  CAS  PubMed  Google Scholar 

  7. Lobley A, Swindells MB, Orengo CA, Jones DT (2007) Inferring function using patterns of native disorder in proteins. PLoS Comput Biol 3(8):e162

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ferron F, Longhi S, Canard B, Karlin D (2006) A practical overview of protein disorder prediction methods. Proteins 65(1):1–14

    Article  CAS  PubMed  Google Scholar 

  9. Ferron F, Rancurel C, Longhi S, Cambillau C, Henrissat B, Canard B (2005) VaZyMolO: a tool to define and classify modularity in viral proteins. J Gen Virol 86(Pt 3):743–749

    Article  CAS  PubMed  Google Scholar 

  10. Lieutaud P, Ferron F, Habchi J, Canard B, Longhi S (2013) Predicting protein disorder and induced folding: a practical approach. In: Dunn B (ed) Advances in protein and peptide sciences, vol 1. Bentham Science Publishers, Beijing, pp 441–492 (452)

    Chapter  Google Scholar 

  11. Bourhis JM, Canard B, Longhi S (2007) Predicting protein disorder and induced folding: from theoretical principles to practical applications. Curr Protein Pept Sci 8(2):135–149

    Article  CAS  PubMed  Google Scholar 

  12. Uversky VN, Radivojac P, Iakoucheva LM, Obradovic Z, Dunker AK (2007) Prediction of intrinsic disorder and its use in functional proteomics. Methods Mol Biol 408:69–92

    Article  CAS  PubMed  Google Scholar 

  13. He B, Wang K, Liu Y, Xue B, Uversky VN, Dunker AK (2009) Predicting intrinsic disorder in proteins: an overview. Cell Res. doi:10.1038/cr.2009.87, cr200987 [pii]

    PubMed Central  Google Scholar 

  14. Longhi S, Lieutaud P, Canard B (2010) Conformational disorder. Methods Mol Biol 609:307–325

    Article  CAS  PubMed  Google Scholar 

  15. 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. doi:10.1002/prot.24391

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ishida T, Kinoshita K (2008) Prediction of disordered regions in proteins based on the meta approach. Bioinformatics 24(11):1344–1348. doi:10.1093/bioinformatics/btn195

    Article  CAS  PubMed  Google Scholar 

  17. Lieutaud P, Canard B, Longhi S (2008) MeDor: a metaserver for predicting protein disorder. BMC Genomics 9(Suppl 2):S25

    Article  PubMed  PubMed Central  Google Scholar 

  18. Brown CJ, Johnson AK, Dunker AK, Daughdrill GW (2011) Evolution and disorder. Curr Opin Struct Biol 21(3):441–446. doi:10.1016/j.sbi.2011.02.005, S0959-440X(11)00036-4 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Oates ME, Romero P, Ishida T, Ghalwash M, Mizianty MJ, Xue B, Dosztanyi Z, Uversky VN, Obradovic Z, Kurgan L, Dunker AK, Gough J (2013) D(2)P(2): database of disordered protein predictions. Nucleic Acids Res 41(Database issue):D508–D516. doi:10.1093/nar/gks1226, gks1226 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Potenza E, Di Domenico T, Walsh I, Tosatto SC (2015) MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins. Nucleic Acids Res 43(Database issue):D315–D320. doi:10.1093/nar/gku982

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sickmeier M, Hamilton JA, LeGall T, Vacic V, Cortese MS, Tantos A, Szabo B, Tompa P, Chen J, Uversky VN, Obradovic Z, Dunker AK (2007) DisProt: the database of disordered proteins. Nucleic Acids Res 35(Database issue):D786–D793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fukuchi S, Amemiya T, Sakamoto S, Nobe Y, Hosoda K, Kado Y, Murakami SD, Koike R, Hiroaki H, Ota M (2014) IDEAL in 2014 illustrates interaction networks composed of intrinsically disordered proteins and their binding partners. Nucleic Acids Res 42(Database issue):D320–D325. doi:10.1093/nar/gkt1010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Varadi M, Kosol S, Lebrun P, Valentini E, Blackledge M, Dunker AK, Felli IC, Forman-Kay JD, Kriwacki RW, Pierattelli R, Sussman J, Svergun DI, Uversky VN, Vendruscolo M, Wishart D, Wright PE, Tompa P (2014) pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins. Nucleic Acids Res 42(Database issue):D326–D335. doi:10.1093/nar/gkt960

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Vucetic S, Brown C, Dunker K, Obradovic Z (2003) Flavors of protein disorder. Proteins 52:573–584

    Article  CAS  PubMed  Google Scholar 

  25. Karlin D, Ferron F, Canard B, Longhi S (2003) Structural disorder and modular organization in Paramyxovirinae N and P. J Gen Virol 84(Pt 12):3239–3252

    Article  CAS  PubMed  Google Scholar 

  26. Severson W, Xu X, Kuhn M, Senutovitch N, Thokala M, Ferron F, Longhi S, Canard B, Jonsson CB (2005) Essential amino acids of the hantaan virus N protein in its interaction with RNA. J Virol 79(15):10032–10039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Llorente MT, Barreno-Garcia B, Calero M, Camafeita E, Lopez JA, Longhi S, Ferron F, Varela PF, Melero JA (2006) Structural analysis of the human respiratory syncitial virus phosphoprotein: characterization of an a-helical domain involved in oligomerization. J Gen Virol 87:159–169

    Article  CAS  PubMed  Google Scholar 

  28. Habchi J, Mamelli L, Darbon H, Longhi S (2010) Structural disorder within henipavirus nucleoprotein and phosphoprotein: from predictions to experimental assessment. PLoS One 5(7):e11684. doi:10.1371/journal.pone.0011684

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kozlowski LP, Bujnicki JM (2012) MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics 13(1):111. doi:10.1186/1471-2105-13-111, 1471-2105-13-111 [pii]

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bordoli L, Kiefer F, Schwede T (2007) Assessment of disorder predictions in CASP7. Proteins 69(Suppl 8):129–136. doi:10.1002/prot.21671

    Article  CAS  PubMed  Google Scholar 

  31. Deng X, Eickholt J, Cheng J (2009) PreDisorder: ab initio sequence-based prediction of protein disordered regions. BMC Bioinformatics 10:436. doi:10.1186/1471-2105-10-436, 1471-2105-10-436 [pii]

    Article  PubMed  PubMed Central  Google Scholar 

  32. Noivirt-Brik O, Prilusky J, Sussman JL (2009) Assessment of disorder predictions in CASP8. Proteins 77(Suppl 9):210–216. doi:10.1002/prot.22586

    Article  CAS  PubMed  Google Scholar 

  33. 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. doi:10.1093/bioinformatics/btq373, btq373 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mizianty MJ, Uversky V, Kurgan L (2014) Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol 1137:147–162. doi:10.1007/978-1-4939-0366-5_11

    Article  CAS  PubMed  Google Scholar 

  35. Xue B, Dunbrack RL, Williams RW, Dunker AK, Uversky VN (2010) PONDR-FIT: a meta-predictor of intrinsically disordered amino acids. Biochim Biophys Acta 1804(4):996–1010. doi:10.1016/j.bbapap.2010.01.011, S1570-9639(10)00013-0 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schlessinger A, Liu J, Rost B (2007) Natively unstructured loops differ from other loops. PLoS Comput Biol 3(7):e140. doi:10.1371/journal.pcbi.0030140

    Article  PubMed  PubMed Central  Google Scholar 

  37. Schlessinger A, Punta M, Rost B (2007) Natively unstructured regions in proteins identified from contact predictions. Bioinformatics 23(18):2376–2384

    Article  CAS  PubMed  Google Scholar 

  38. Schlessinger A, Punta M, Yachdav G, Kajan L, Rost B (2009) Improved disorder prediction by combination of orthogonal approaches. PLoS One 4(2):e4433. doi:10.1371/journal.pone.0004433

    Article  PubMed  PubMed Central  Google Scholar 

  39. Schlessinger A, Yachdav G, Rost B (2006) PROFbval: predict flexible and rigid residues in proteins. Bioinformatics 22(7):891–893. doi:10.1093/bioinformatics/btl032

    Article  CAS  PubMed  Google Scholar 

  40. Chandonia JM (2007) StrBioLib: a Java library for development of custom computational structural biology applications. Bioinformatics 23(15):2018–2020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Eickholt J, Cheng J (2013) DNdisorder: predicting protein disorder using boosting and deep networks. BMC Bioinformatics 14:88. doi:10.1186/1471-2105-14-88

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Romero P, Obradovic Z, Li X, Garner EC, Brown CJ, Dunker AK (2001) Sequence complexity of disordered proteins. Proteins 42(1):38–48

    Article  CAS  PubMed  Google Scholar 

  43. Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker AK (2005) Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins 61(Suppl 7):176–182

    Article  CAS  PubMed  Google Scholar 

  44. Obradovic Z, Peng K, Vucetic S, Radivojac P, Brown CJ, Dunker AK (2003) Predicting intrinsic disorder from amino acid sequence. Proteins 53(Suppl 6):566–572

    Article  CAS  PubMed  Google Scholar 

  45. Peng K, Vucetic S, Radivojac P, Brown CJ, Dunker AK, Obradovic Z (2005) Optimizing long intrinsic disorder predictors with protein evolutionary information. J Bioinformatics Comput Biol 3(1):35–60

    Article  CAS  Google Scholar 

  46. Linding R, Russell RB, Neduva V, Gibson TJ (2003) GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res 31(13):3701–3708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Linding R, Jensen LJ, Diella F, Bork P, Gibson TJ, Russell RB (2003) Protein disorder prediction: implications for structural proteomics. Structure (Camb) 11(11):1453–1459

    Article  CAS  Google Scholar 

  48. Ward JJ, McGuffin LJ, Bryson K, Buxton BF, Jones DT (2004) The DISOPRED server for the prediction of protein disorder. Bioinformatics 20(13):2138–2139

    Article  CAS  PubMed  Google Scholar 

  49. Yang ZR, Thomson R, McNeil P, Esnouf RM (2005) RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21(16):3369–3376

    Article  CAS  PubMed  Google Scholar 

  50. Cheng J, Sweredoski M, Baldi P (2005) Accurate prediction of protein disordered regions by mining protein structure data. Data Min Knowl Disc 11:213–222

    Article  Google Scholar 

  51. Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21(8):1719–1720

    Article  CAS  PubMed  Google Scholar 

  52. Walsh I, Martin AJ, Di Domenico T, Tosatto SC (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28(4):503–509. doi:10.1093/bioinformatics/btr682

    Article  CAS  PubMed  Google Scholar 

  53. Zhang T, Faraggi E, Xue B, Dunker AK, Uversky VN, Zhou Y (2012) SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method. J Biomol Struct Dyn 29(4):799–813, c4317/SPINE-D-Accurate-Prediction-of-Short-and-Long-Disordered-Regions-by-a-Singl e-Neural-Network-Based-Method-p18379.html [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Fukuchi S, Hosoda K, Homma K, Gojobori T, Nishikawa K (2011) Binary classification of protein molecules into intrinsically disordered and ordered segments. BMC Struct Biol 11:29. doi:10.1186/1472-6807-11-29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang L, Sauer UH (2008) OnD-CRF: predicting order and disorder in proteins using [corrected] conditional random fields. Bioinformatics 24(11):1401–1402. doi:10.1093/bioinformatics/btn132, btn132 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ishida T, Kinoshita K (2007) PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res 35(Web Server issue):W460–W464. doi:10.1093/nar/gkm363, gkm363 [pii]

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hirose S, Shimizu K, Noguchi T (2010) POODLE-I: disordered region prediction by integrating POODLE series and structural information predictors based on a workflow approach. Silico Biol 10(3):185–191. doi:10.3233/ISB-2010-0426, X5W186151360G147 [pii]

    Google Scholar 

  58. Dosztanyi Z, Csizmok V, Tompa P, Simon I (2005) IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 21(16):3433–3434

    Article  CAS  PubMed  Google Scholar 

  59. Galzitskaya OV, Garbuzynskiy SO, Lobanov MY (2006) FoldUnfold: web server for the prediction of disordered regions in protein chain. Bioinformatics 22(23):2948–2949

    Article  CAS  PubMed  Google Scholar 

  60. Uversky VN, Gillespie JR, Fink AL (2000) Why are “natively unfolded” proteins unstructured under physiologic conditions? Proteins 41(3):415–427

    Article  CAS  PubMed  Google Scholar 

  61. Zeev-Ben-Mordehai T, Rydberg EH, Solomon A, Toker L, Auld VJ, Silman I, Botti S, Sussman JL (2003) The intracellular domain of the Drosophila cholinesterase-like neural adhesion protein, gliotactin, is natively unfolded. Proteins 53(3):758–767

    Article  CAS  PubMed  Google Scholar 

  62. Oldfield CJ, Cheng Y, Cortese MS, Brown CJ, Uversky VN, Dunker AK (2005) Comparing and combining predictors of mostly disordered proteins. Biochemistry 44(6):1989–2000

    Article  CAS  PubMed  Google Scholar 

  63. Xue B, Oldfield CJ, Dunker AK, Uversky VN (2009) CDF it all: consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions. FEBS Lett 583(9):1469–1474. doi:10.1016/j.febslet.2009.03.070, S0014-5793(09)00260-9 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Mohan A, Sullivan WJ Jr, Radivojac P, Dunker AK, Uversky VN (2008) Intrinsic disorder in pathogenic and non-pathogenic microbes: discovering and analyzing the unfoldomes of early-branching eukaryotes. Mol Biosyst 4(4):328–340

    Article  CAS  PubMed  Google Scholar 

  65. Callebaut I, Labesse G, Durand P, Poupon A, Canard L, Chomilier J, Henrissat B, Mornon JP (1997) Deciphering protein sequence information through hydrophobic cluster analysis (HCA): current status and perspectives. Cell Mol Life Sci 53(8):621–645

    Article  CAS  PubMed  Google Scholar 

  66. Blocquel D, Habchi J, Gruet A, Blangy S, Longhi S (2012) Compaction and binding properties of the intrinsically disordered C-terminal domain of Henipavirus nucleoprotein as unveiled by deletion studies. Mol Biosyst 8(1):392–410. doi:10.1039/c1mb05401e

    Article  CAS  PubMed  Google Scholar 

  67. Uversky VN (2002) Natively unfolded proteins: a point where biology waits for physics. Protein Sci 11(4):739–756

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Oldfield CJ, Cheng Y, Cortese MS, Romero P, Uversky VN, Dunker AK (2005) Coupled folding and binding with alpha-helix-forming molecular recognition elements. Biochemistry 44(37):12454–12470

    Article  CAS  PubMed  Google Scholar 

  69. Cheng Y, Oldfield CJ, Meng J, Romero P, Uversky VN, Dunker AK (2007) Mining alpha-helix-forming molecular recognition features with cross species sequence alignments. Biochemistry 46(47):13468–13477. doi:10.1021/bi7012273

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Vacic V, Oldfield CJ, Mohan A, Radivojac P, Cortese MS, Uversky VN, Dunker AK (2007) Characterization of molecular recognition features, MoRFs, and their binding partners. J Proteome Res 6(6):2351–2366

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Meszaros B, Simon I, Dosztanyi Z (2009) Prediction of protein binding regions in disordered proteins. PLoS Comput Biol 5(5):e1000376. doi:10.1371/journal.pcbi.1000376

    Article  PubMed  PubMed Central  Google Scholar 

  72. Bourhis J, Johansson K, Receveur-Bréchot V, Oldfield CJ, Dunker AK, Canard B, Longhi S (2004) The C-terminal domain of measles virus nucleoprotein belongs to the class of intrinsically disordered proteins that fold upon binding to their physiological partner. Virus Res 99:157–167

    Article  CAS  PubMed  Google Scholar 

  73. John SP, Wang T, Steffen S, Longhi S, Schmaljohn CS, Jonsson CB (2007) Ebola virus VP30 is an RNA binding protein. J Virol 81(17):8967–8976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Meszaros B, Tompa P, Simon I, Dosztanyi Z (2007) Molecular principles of the interactions of disordered proteins. J Mol Biol 372(2):549–561

    Article  CAS  PubMed  Google Scholar 

  75. Habchi J, Blangy S, Mamelli L, Ringkjobing Jensen M, Blackledge M, Darbon H, Oglesbee M, Shu Y, Longhi S (2011) Characterization of the interactions between the nucleoprotein and the phosphoprotein of Henipaviruses. J Biol Chem 286(15):13583–13602

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Dosztanyi Z, Meszaros B, Simon I (2009) ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics 25(20):2745–2746. doi:10.1093/bioinformatics/btp518, btp518 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 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. doi:10.1093/bioinformatics/bts209, bts209 [pii]

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405

    Article  CAS  PubMed  Google Scholar 

  79. Wootton JC (1994) Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem 18(3):269–285

    Article  CAS  PubMed  Google Scholar 

  80. Kall L, Krogh A, Sonnhammer EL (2007) Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res 35(Web Server issue):W429–W432

    Article  PubMed  PubMed Central  Google Scholar 

  81. Bornberg-Bauer E, Rivals E, Vingron M (1998) Computational approaches to identify leucine zippers. Nucleic Acids Res 26(11):2740–2746

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lupas A, Van Dyke M, Stock J (1991) Predicting coiled coils from protein sequences. Science 252(5009):1162–1164

    Article  CAS  PubMed  Google Scholar 

  83. Baldi P, Cheng J, Vullo A (2004) Large-scale prediction of disulphide bond connectivity. Adv Neural Inf Process Syst 17:97–104

    Google Scholar 

  84. Eudes R, Le Tuan K, Delettre J, Mornon JP, Callebaut I (2007) A generalized analysis of hydrophobic and loop clusters within globular protein sequences. BMC Struct Biol 7:2. doi:10.1186/1472-6807-7-2

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AFMB UMR 7257, Aix-Marseille Université, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France

    Philippe Lieutaud, François Ferron & Sonia Longhi

  2. AFMB UMR 7257, CNRS, 163, avenue de Luminy, Case 932, 13288, Marseille Cedex 09, France

    Philippe Lieutaud, François Ferron & Sonia Longhi

Authors
  1. Philippe Lieutaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. François Ferron
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sonia Longhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sonia Longhi .

Editor information

Editors and Affiliations

  1. Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

    Oliviero Carugo

  2. Technology and Research (A*STAR), Agency for Science, Singapore, Singapore

    Frank Eisenhaber

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this protocol

Cite this protocol

Lieutaud, P., Ferron, F., Longhi, S. (2016). Predicting Conformational Disorder. In: Carugo, O., Eisenhaber, F. (eds) Data Mining Techniques for the Life Sciences. Methods in Molecular Biology, vol 1415. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3572-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-3572-7_14

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3570-3

  • Online ISBN: 978-1-4939-3572-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation