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Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor

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Intrinsically Disordered Proteins

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2141))

Abstract

Intrinsically disordered proteins are either entirely disordered or contain disordered regions in their native state. These proteins and regions function without the prerequisite of a stable structure and were found to be abundant across all kingdoms of life. Experimental annotation of disorder lags behind the rapidly growing number of sequenced proteins, motivating the development of computational methods that predict disorder in protein sequences. DisCoP is a user-friendly webserver that provides accurate sequence-based prediction of protein disorder. It relies on meta-architecture in which the outputs generated by multiple disorder predictors are combined together to improve predictive performance. The architecture of disCoP is presented, and its accuracy relative to several other disorder predictors is briefly discussed. We describe usage of the web interface and explain how to access and read results generated by this computational tool. We also provide an example of prediction results and interpretation. The disCoP’s webserver is publicly available at http://biomine.cs.vcu.edu/servers/disCoP/.

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References

  1. Dunker AK, Babu MM, Barbar E et al (2013) What’s in a name? Why these proteins are intrinsically disordered. Intrinsically Disord Proteins 1:e24157

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dunker AK, Obradovic Z (2001) The protein trinity--linking function and disorder. Nat Biotechnol 19:805–806

    Article  CAS  PubMed  Google Scholar 

  3. Habchi J, Tompa P, Longhi S et al (2014) Introducing protein intrinsic disorder. Chem Rev 114:6561–6588

    Article  CAS  PubMed  Google Scholar 

  4. Lieutaud P, Ferron F, Uversky AV et al (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:e1259708

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  6. Wright PE, Dyson HJ (1999) Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm. J Mol Biol 293:321–331

    Article  CAS  PubMed  Google Scholar 

  7. Dunker AK, Obradovic Z, Romero P et al (2000) Intrinsic protein disorder in complete genomes. Genome Inform Ser Workshop Genome Inform 11:161–171

    CAS  PubMed  Google Scholar 

  8. Oates ME, Romero P, Ishida T et al (2013) D(2)P(2): database of disordered protein predictions. Nucleic Acids Res 41:D508–D516

    Article  CAS  PubMed  Google Scholar 

  9. Peng Z, Mizianty MJ, Kurgan L (2014) Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins 82:145–158

    Article  CAS  PubMed  Google Scholar 

  10. Peng Z, Yan J, Fan X et al (2015) Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life. Cell Mol Life Sci 72:137–151

    Article  CAS  PubMed  Google Scholar 

  11. Ward JJ, Sodhi JS, Mcguffin LJ et al (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337:635–645

    Article  CAS  PubMed  Google Scholar 

  12. 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 30:137–149

    Article  CAS  PubMed  Google Scholar 

  13. Yan J, Mizianty MJ, Filipow PL et al (2013) RAPID: fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale. Biochim Biophys Acta 1834:1671–1680

    Article  CAS  PubMed  Google Scholar 

  14. Charon J, Theil S, Nicaise V et al (2016) Protein intrinsic disorder within the Potyvirus genus: from proteome-wide analysis to functional annotation. Mol BioSyst 12:634–652

    Article  CAS  PubMed  Google Scholar 

  15. Fan X, Xue B, Dolan PT et al (2014) The intrinsic disorder status of the human hepatitis C virus proteome. Mol BioSyst 10:1345–1363

    Article  CAS  PubMed  Google Scholar 

  16. Meng F, Badierah RA, Almehdar HA et al (2015) Unstructural biology of the Dengue virus proteins. FEBS J 282:3368–3394

    Article  CAS  PubMed  Google Scholar 

  17. 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 16:1486–1498

    Article  CAS  PubMed  Google Scholar 

  18. Xue B, Blocquel D, Habchi J et al (2014) Structural disorder in viral proteins. Chem Rev 114:6880–6911

    Article  CAS  PubMed  Google Scholar 

  19. Xue B, Mizianty MJ, Kurgan L et al (2012) Protein intrinsic disorder as a flexible armor and a weapon of HIV-1. Cell Mol Life Sci 69:1211–1259

    Article  CAS  PubMed  Google Scholar 

  20. Xue B, Williams RW, Oldfield CJ et al (2010) Archaic chaos: intrinsically disordered proteins in Archaea. BMC Syst Biol 4(Suppl 1):S1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Dunker AK, Cortese MS, Romero P et al (2005) Flexible nets. The roles of intrinsic disorder in protein interaction networks. FEBS J 272:5129–5148

    Article  CAS  PubMed  Google Scholar 

  22. Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6:197–208

    Article  CAS  PubMed  Google Scholar 

  23. Galea CA, Wang Y, Sivakolundu SG et al (2008) Regulation of cell division by intrinsically unstructured proteins: intrinsic flexibility, modularity, and signaling conduits. Biochemistry 47:7598–7609

    Article  CAS  PubMed  Google Scholar 

  24. Uversky VN, Oldfield CJ, Dunker AK (2005) Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling. J Mol Recognit 18:343–384

    Article  CAS  PubMed  Google Scholar 

  25. Fuxreiter M, Tompa P, Simon I et al (2008) Malleable machines take shape in eukaryotic transcriptional regulation. Nat Chem Biol 4:728–737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu J, Perumal NB, Oldfield CJ et al (2006) Intrinsic disorder in transcription factors. Biochemistry 45:6873–6888

    Article  CAS  PubMed  Google Scholar 

  27. Peng Z, Oldfield CJ, Xue B et al (2014) A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome. Cell Mol Life Sci 71:1477–1504

    Article  CAS  PubMed  Google Scholar 

  28. Dyson HJ (2012) Roles of intrinsic disorder in protein-nucleic acid interactions. Mol BioSyst 8:97–104

    Article  CAS  PubMed  Google Scholar 

  29. Meng F, Na I, Kurgan L et al (2016) Compartmentalization and functionality of nuclear disorder: intrinsic disorder and protein-protein interactions in intra-nuclear compartments. Int J Mol Sci 17:E24

    Article  CAS  Google Scholar 

  30. Peng Z, Mizianty MJ, Xue B et al (2012) More than just tails: intrinsic disorder in histone proteins. Mol BioSyst 8:1886–1901

    Article  CAS  PubMed  Google Scholar 

  31. Sandhu KS (2009) Intrinsic disorder explains diverse nuclear roles of chromatin remodeling proteins. J Mol Recognit 22:1–8

    Article  CAS  PubMed  Google Scholar 

  32. Chen JW, Romero P, Uversky VN et al (2006) Conservation of intrinsic disorder in protein domains and families: II. Functions of conserved disorder. J Proteome Res 5:888–898

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. 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

    Article  PubMed  CAS  Google Scholar 

  34. Cumberworth A, Lamour G, Babu MM et al (2013) Promiscuity as a functional trait: intrinsically disordered regions as central players of interactomes. Biochem J 454:361–369

    Article  CAS  PubMed  Google Scholar 

  35. Fuxreiter M, Toth-Petroczy A, Kraut DA et al (2014) Disordered proteinaceous machines. Chem Rev 114:6806–6843

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Haynes C, Oldfield CJ, Ji F et al (2006) Intrinsic disorder is a common feature of hub proteins from four eukaryotic interactomes. PLoS Comput Biol 2:890–901

    Article  CAS  Google Scholar 

  37. Peng Z, Sakai Y, Kurgan L et al (2014) Intrinsic disorder in the BK channel and its interactome. PLoS One 9:e94331

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Tompa P, Csermely P (2004) The role of structural disorder in the function of RNA and protein chaperones. FASEB J 18:1169–1175

    Article  CAS  PubMed  Google Scholar 

  39. Wu Z, Hu G, Yang J et al (2015) In various protein complexes, disordered protomers have large per-residue surface areas and area of protein-, DNA- and RNA-binding interfaces. FEBS Lett 589:2561–2569

    Article  CAS  PubMed  Google Scholar 

  40. Buljan M, Chalancon G, Dunker AK et al (2013) Alternative splicing of intrinsically disordered regions and rewiring of protein interactions. Curr Opin Struct Biol 23:443–450

    Article  CAS  PubMed  Google Scholar 

  41. Korneta I, Bujnicki JM (2012) Intrinsic disorder in the human spliceosomal proteome. PLoS Comput Biol 8:e1002641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Romero PR, Zaidi S, Fang YY et al (2006) Alternative splicing in concert with protein intrinsic disorder enables increased functional diversity in multicellular organisms. Proc Natl Acad Sci U S A 103:8390–8395

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhou JH, Zhao SW, Dunker AK (2018) Intrinsically disordered proteins link alternative splicing and post-translational modifications to complex cell signaling and regulation. J Mol Biol 430:2342–2359

    Article  CAS  PubMed  Google Scholar 

  44. Kurotani A, Tokmakov AA, Kuroda Y et al (2014) Correlations between predicted protein disorder and post-translational modifications in plants. Bioinformatics 30:1095–1103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Xie H, Vucetic S, Iakoucheva LM et al (2007) Functional anthology of intrinsic disorder. 3. Ligands, post-translational modifications, and diseases associated with intrinsically disordered proteins. J Proteome Res 6:1917–1932

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cheng Y, Legall T, Oldfield CJ et al (2006) Rational drug design via intrinsically disordered protein. Trends Biotechnol 24:435–442

    Article  CAS  PubMed  Google Scholar 

  47. Hu G, Wu Z, Wang K et al (2016) Untapped potential of disordered proteins in current druggable human proteome. Curr Drug Targets 17:1198–1205

    Article  CAS  PubMed  Google Scholar 

  48. Midic U, Oldfield CJ, Dunker AK et al (2009) Unfoldomics of human genetic diseases: illustrative examples of ordered and intrinsically disordered members of the human diseasome. Protein Pept Lett 16:1533–1547

    Article  CAS  PubMed  Google Scholar 

  49. Uversky VN, Oldfield CJ, Dunker AK (2008) Intrinsically disordered proteins in human diseases: introducing the D2 concept. Annu Rev Biophys 37:215–246

    Article  CAS  PubMed  Google Scholar 

  50. Campen A, Williams RM, Brown CJ et al (2008) TOP-IDP-scale: a new amino acid scale measuring propensity for intrinsic disorder. Protein Pept Lett 15:956–963

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Li X, Romero P, Rani M et al (1999) Predicting protein disorder for N-, C-, and internal regions. Genome Inform Ser Workshop Genome Inform 10:30–40

    CAS  PubMed  Google Scholar 

  52. Romero P, Obradovic Z, Kissinger C et al (1997) Identifying disordered regions in proteins from amino acid sequence. Int Conf Neural Netw 91:90–95

    Google Scholar 

  53. Romero P, Obradovic Z, Li X et al (2001) Sequence complexity of disordered protein. Proteins 42:38–48

    Article  CAS  PubMed  Google Scholar 

  54. Atkins J, Boateng S, Sorensen T et al (2015) Disorder prediction methods, their applicability to different protein targets and their usefulness for guiding experimental studies. Int J Mol Sci 16:19040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Deng X, Eickholt J, Cheng J (2012) A comprehensive overview of computational protein disorder prediction methods. Mol BioSyst 8:114–121

    Article  CAS  PubMed  Google Scholar 

  56. Dosztányi Z, Mészáros B, Simon I (2010) Bioinformatical approaches to characterize intrinsically disordered/unstructured proteins. Brief Bioinform 11:225–243

    Article  PubMed  CAS  Google Scholar 

  57. Dosztányi Z, Tompa P (2008) Prediction of protein disorder. In: Kobe B, Guss M, Huber T (eds) Structural proteomics. Humana Press, Totowa, New Jersey, pp 103–115

    Chapter  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  59. He B, Wang K, Liu Y et al (2009) Predicting intrinsic disorder in proteins: an overview. Cell Res 19:929–949

    Article  CAS  PubMed  Google Scholar 

  60. Li J, Feng Y, Wang X et al (2015) An overview of predictors for intrinsically disordered proteins over 2010–2014. Int J Mol Sci 16:23446

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Meng F, Uversky V, Kurgan L (2017) Computational prediction of intrinsic disorder in proteins. Curr Protoc Protein Sci 88:2 16 11–12 16 14

    Google Scholar 

  62. Meng F, Uversky VN, Kurgan L (2017) Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell Mol Life Sci 74:3069–3090

    Article  CAS  PubMed  Google Scholar 

  63. Monastyrskyy B, Kryshtafovych A, Moult J et al (2014) Assessment of protein disorder region predictions in CASP10. Proteins 82(Suppl 2):127–137

    Article  CAS  PubMed  Google Scholar 

  64. Necci M, Piovesan D, Dosztanyi Z et al (2017) A comprehensive assessment of long intrinsic protein disorder from the DisProt database. Bioinformatics 34(3):445–452

    Article  CAS  Google Scholar 

  65. Pentony M, Ward J, Jones D (2010) Computational resources for the prediction and analysis of native disorder in proteins. In: Hubbard SJ, Jones AR (eds) Proteome bioinformatics. Humana Press, Totowa, New Jersey, pp 369–393

    Chapter  Google Scholar 

  66. Peng K, Radivojac P, Vucetic S et al (2006) Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 7:208

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Ishida T, Kinoshita K (2008) Prediction of disordered regions in proteins based on the meta approach. Bioinformatics 24:1344–1348

    Article  CAS  PubMed  Google Scholar 

  68. Deng X, Eickholt J, Cheng J (2009) PreDisorder: ab initio sequence-based prediction of protein disordered regions. BMC Bioinformatics 10:436

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Xue B, Oldfield CJ, Dunker AK et al (2009) CDF it all: consensus prediction of intrinsically disordered proteins based on various cumulative distribution functions. FEBS Lett 583:1469–1474

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Schlessinger A, Punta M, Yachdav G et al (2009) Improved disorder prediction by combination of orthogonal approaches. PLoS One 4:e4433

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Xue B, Dunbrack RL, Williams RW et al (2010) PONDR-FIT: a meta-predictor of intrinsically disordered amino acids. Biochim Biophys Acta 1804:996–1010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mizianty MJ, Stach W, Chen K et al (2010) Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources. Bioinformatics 26:i489–i496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Walsh I, Martin AJM, Di Domenico T et al (2011) CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs. Nucleic Acids Res 39:W190–W196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Kozlowski LP, Bujnicki JM (2012) MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins. BMC Bioinformatics 13:1–11

    Article  Google Scholar 

  75. Walsh I, Martin AJM, Di Domenico T et al (2012) ESpritz: accurate and fast prediction of protein disorder. Bioinformatics 28:503–509

    Article  CAS  PubMed  Google Scholar 

  76. Mizianty MJ, Peng ZL, Kurgan L (2013) MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles. Intrinsically Disordered Proteins 1:e24428

    Article  PubMed  PubMed Central  Google Scholar 

  77. Mizianty MJ, Uversky V, Kurgan L (2014) Prediction of intrinsic disorder in proteins using MFDp2. Methods Mol Biol 1137:147–162

    Article  CAS  PubMed  Google Scholar 

  78. Huang YJ, Acton TB, Montelione GT (2014) DisMeta: a meta server for construct design and optimization. Methods Mol Biol 1091:3–16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Fan X, Kurgan L (2014) Accurate prediction of disorder in protein chains with a comprehensive and empirically designed consensus. J Biomol Struct Dyn 32:448–464

    Article  CAS  PubMed  Google Scholar 

  80. Jones DT, Cozzetto D (2015) DISOPRED3: precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31:857–863

    Article  CAS  PubMed  Google Scholar 

  81. Necci M, Piovesan D, Dosztanyi Z et al (2017) MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins. Bioinformatics 33:1402–1404

    CAS  PubMed  Google Scholar 

  82. Peng Z, Kurgan L (2012) On the complementarity of the consensus-based disorder prediction. Pac Symp Biocomput:176–187

    Google Scholar 

  83. Zhang T, Faraggi E, Xue B et al (2012) SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method. J Biomol Struct Dyn 29:799–813

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Mcguffin LJ (2008) Intrinsic disorder prediction from the analysis of multiple protein fold recognition models. Bioinformatics 24:1798–1804

    Article  CAS  PubMed  Google Scholar 

  85. Ward JJ, Mcguffin LJ, Bryson K et al (2004) The DISOPRED server for the prediction of protein disorder. Bioinformatics 20:2138–2139

    Article  CAS  PubMed  Google Scholar 

  86. Hartlepp KF, Fernandez-Tornero C, Eberharter A et al (2005) The histone fold subunits of Drosophila CHRAC facilitate nucleosome sliding through dynamic DNA interactions. Mol Cell Biol 25:9886–9896

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Wang C, Kurgan L (2018) Review and comparative assessment of similarity-based methods for prediction of drug-protein interactions in the druggable human proteome. Brief Bioinform 20(6):2066–2087

    Article  CAS  Google Scholar 

  88. Kurgan L, Razib AA, Aghakhani S et al (2009) CRYSTALP2: sequence-based protein crystallization propensity prediction. BMC Struct Biol 9:50

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Kedarisetti P, Mizianty MJ, Kaas Q et al (2014) Prediction and characterization of cyclic proteins from sequences in three domains of life. Biochim Biophys Acta 1844:181–190

    Article  CAS  PubMed  Google Scholar 

  90. Meng F, Kurgan L (2016) DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences. Bioinformatics 32:i341–i350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Mizianty MJ, Zhang T, Xue B et al (2011) In-silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics 12:245

    Article  PubMed  PubMed Central  Google Scholar 

  92. Peng Z, Kurgan L (2015) High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Res 43:e121

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Peng Z, Wang C, Uversky VN et al (2017) Prediction of disordered RNA, DNA, and protein binding regions using DisoRDPbind. Methods Mol Biol 1484:187–203

    Article  CAS  PubMed  Google Scholar 

  94. Meng F, Kurgan L (2018) High-throughput prediction of disordered moonlighting regions in protein sequences. Proteins 86(10):1097–1110

    Article  CAS  PubMed  Google Scholar 

  95. Yan J, Kurgan L (2017) DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues. Nucleic Acids Res 45:e84

    PubMed  PubMed Central  Google Scholar 

  96. Meng F, Wang C, Kurgan L (2018) fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization. BMC Bioinformatics 18:580

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Mizianty MJ, Fan X, Yan J et al (2014) Covering complete proteomes with X-ray structures: a current snapshot. Acta Crystallogr D Biol Crystallogr 70:2781–2793

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Yan J, Dunker AK, Uversky VN et al (2016) Molecular recognition features (MoRFs) in three domains of life. Mol BioSyst 12:697–710

    Article  CAS  PubMed  Google Scholar 

  99. Amirkhani A, Kolahdoozi M, Wang C et al (2018) Prediction of DNA-binding residues in local segments of protein sequences with Fuzzy Cognitive Maps. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2018.2890261

  100. Zhang J, Ma Z, Kurgan L (2017) Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains. Brief Bioinform 20(4):1250–1268

    Article  CAS  Google Scholar 

  101. Hu G, Gao J, Wang K et al (2012) Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions. Structure 20:1815–1822

    Article  CAS  PubMed  Google Scholar 

  102. Disfani FM, Hsu WL, Mizianty MJ et al (2012) MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins. Bioinformatics 28:i75–i83

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Oldfield CJ, Uversky VN, Kurgan L (2018) Predicting functions of disordered proteins with MoRFpred. Methods Mol Biol 1851:337–352

    Article  CAS  Google Scholar 

  104. Chen K, Mizianty MJ, Kurgan L (2012) Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors. Bioinformatics 28:331–341

    Article  PubMed  CAS  Google Scholar 

  105. Mizianty MJ, Kurgan L (2011) Sequence-based prediction of protein crystallization, purification and production propensity. Bioinformatics 27:i24–i33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Hu G, Wu Z, Oldfield C et al (2018) Quality assessment for the putative intrinsic disorder in proteins. Bioinformatics 35(10):1692–1700

    Article  CAS  Google Scholar 

  107. Wu Z, Hu G, Wang K et al (2017) Exploratory analysis of quality assessment of putative intrinsic disorder in proteins. In: 6th International Conference on Artificial Intelligence and Soft Computing. Zakopane, Poland, pp 722–732

    Google Scholar 

  108. Yan J, Marcus M, Kurgan L (2014) Comprehensively designed consensus of standalone secondary structure predictors improves Q3 by over 3%. J Biomol Struct Dyn 32:36–51

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA

    Christopher J. Oldfield & Lukasz Kurgan

  2. Department of Pediatrics, Columbia University, New York, NY, USA

    Xiao Fan

  3. Department of Medicine, Columbia University, New York, NY, USA

    Chen Wang

  4. Department of Biochemistry and Molecular Biology, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA

    A. Keith Dunker

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  1. Christopher J. Oldfield
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  2. Xiao Fan
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  3. Chen Wang
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  4. A. Keith Dunker
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  5. Lukasz Kurgan
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Correspondence to Lukasz Kurgan .

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Editors and Affiliations

  1. Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, København N, Denmark

    Birthe B. Kragelund

  2. Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Karen Skriver

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Oldfield, C.J., Fan, X., Wang, C., Dunker, A.K., Kurgan, L. (2020). Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor. In: Kragelund, B.B., Skriver, K. (eds) Intrinsically Disordered Proteins. Methods in Molecular Biology, vol 2141. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0524-0_2

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  • DOI: https://doi.org/10.1007/978-1-0716-0524-0_2

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