DisMeta: A Meta Server for Construct Design and Optimization

  • Yuanpeng Janet Huang
  • Thomas B. Acton
  • Gaetano T. Montelione
Part of the Methods in Molecular Biology book series (MIMB, volume 1091)


Intrinsically disordered or unstructured regions in proteins are both common and biologically important, particularly in regulation, signaling, and modulating intermolecular recognition processes. From a practical point of view, however, such disordered regions often can pose significant challenges for crystallization. Disordered regions are also detrimental to NMR spectral quality, complicating the analysis of resonance assignments and three-dimensional protein structures by NMR methods. The DisMeta Server has been used by Northeastern Structural Genomics (NESG) consortium as a primary tool for construct design and optimization in preparing samples for both NMR and crystallization studies. It is a meta-server that generates a consensus analysis of eight different sequence-based disorder predictors to identify regions that are likely to be disordered. DisMeta also identifies predicted secretion signal peptides, transmembrane segments, and low-complexity regions. Identification of disordered regions, by either experimental or computational methods, is an important step in the NESG structure production pipeline, allowing the rational design of protein constructs that have improved expression and solubility, improved crystallization, and better quality NMR spectra.

Key words

Intrinsically disorder protein prediction Construct design Construct optimization Hydrogen–deuterium exchange with mass spectrometry (HDX-MS) 



We thank H. Zheng, S. Sharma, A. Ertekin, and R. Xiao for providing the HDX-MS data illustrated in this chapter and J. Aramini for providing the NMR spectrum shown in Fig. 5. The NMR data shown in Fig. 3 were recorded by P. Rossi. This work was supported by a grant from the National Institute of General Medical Sciences Protein Structure Initiative U54-GM074958 (to G.T.M.).


  1. 1.
    Dyson HJ, Wright PE (2002) Coupling of folding and binding for unstructured proteins. Curr Opin Struct Biol 12:54–60CrossRefPubMedGoogle Scholar
  2. 2.
    Iakoucheva LM, Brown CJ, Lawson JD et al (2002) Intrinsic disorder in cell-signaling and cancer-associated proteins. J Mol Biol 323:573–584CrossRefPubMedGoogle Scholar
  3. 3.
    Radivojac P, Iakoucheva LM, Oldfield CJ et al (2007) Intrinsic disorder and functional proteomics. Biophys J 92:1439–1456CrossRefPubMedGoogle Scholar
  4. 4.
    Kovacs D, Szabo B, Pancsa R et al (2012) Intrinsically disordered proteins undergo and assist folding transitions in the proteome. Arch Biochem Biophys 531:80–89CrossRefPubMedGoogle Scholar
  5. 5.
    Liu J, Perumal NB, Oldfield CJ et al (2006) Intrinsic disorder in transcription factors. Biochemistry 45:6873–6888CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Minezaki Y, Homma K, Kinjo AR et al (2006) Human transcription factors contain a high fraction of intrinsically disordered regions essential for transcriptional regulation. J Mol Biol 359:1137–1149CrossRefPubMedGoogle Scholar
  7. 7.
    Balazs A, Csizmok V, Buday L et al (2009) High levels of structural disorder in scaffold proteins as exemplified by a novel neuronal protein, CASK-interactive protein1. FEBS J 276:3744–3756CrossRefPubMedGoogle Scholar
  8. 8.
    Buday L, Tompa P (2010) Functional classification of scaffold proteins and related molecules. FEBS J 277:4348–4355CrossRefPubMedGoogle Scholar
  9. 9.
    Tompa P, Csermely P (2004) The role of structural disorder in the function of RNA and protein chaperones. FASEB J 18:1169–1175CrossRefPubMedGoogle Scholar
  10. 10.
    Dosztanyi Z, Chen J, Dunker AK et al (2006) Disorder and sequence repeats in hub proteins and their implications for network evolution. J Proteome Res 5:2985–2995CrossRefPubMedGoogle Scholar
  11. 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:e100Google Scholar
  12. 12.
    Pantazatos D, Kim JS, Klock HE et al (2004) Rapid refinement of crystallographic protein construct definition employing enhanced hydrogen/deuterium exchange MS. Proc Natl Acad Sci USA 101:751–756CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Spraggon G, Pantazatos D, Klock HE et al (2004) On the use of DXMS to produce more crystallizable proteins: structures of the T. maritima proteins TM0160 and TM1171. Protein Sci 13:3187–3199CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Sharma S, Zheng H, Huang YJ et al (2009) Construct optimization for protein NMR structure analysis using amide hydrogen/deuterium exchange mass spectrometry. Proteins 76:882–894CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Netzer WJ, Hartl FU (1997) Recombination of protein domains facilitated by co-translational folding in eukaryotes. Nature 388:343–349CrossRefPubMedGoogle Scholar
  16. 16.
    Emanuelsson O, Brunak S, von Heijne G et al (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953–971CrossRefPubMedGoogle Scholar
  17. 17.
    Krogh A, Larsson B, von Heijne G et al (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580CrossRefPubMedGoogle Scholar
  18. 18.
    Wootton JC, Federhen S (1996) Analysis of compositionally biased regions in sequence databases. Methods Enzymol 266:554–571CrossRefPubMedGoogle Scholar
  19. 19.
    Rost B, Yachdav G, Liu J (2004) The PredictProtein server. Nucleic Acids Res 32:W321–W326CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16:404–405CrossRefPubMedGoogle Scholar
  21. 21.
    Dosztanyi Z, Meszaros B, Simon I (2009) ANCHOR: web server for predicting protein binding regions in disordered proteins. Bioinformatics 25:2745–2746CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Dessailly BH, Nair R, Jaroszewski L et al (2009) PSI-2: structural genomics to cover protein domain family space. Structure 17:869–881CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hunter S, Jones P, Mitchell A et al (2012) InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res 40:D306–D312CrossRefPubMedGoogle Scholar
  24. 24.
    Graslund S, Sagemark J, Berglund H et al (2008) The use of systematic N- and C-terminal deletions to promote production and structural studies of recombinant proteins. Protein Expr Purif 58:210–221CrossRefPubMedGoogle Scholar
  25. 25.
    Chikayama E, Kurotani A, Tanaka T et al (2010) Mathematical model for empirically optimizing large scale production of soluble protein domains. BMC Bioinformatics 11:113CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Xiao R, Anderson S, Aramini J et al (2010) The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium. J Struct Biol 172:21–33CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Acton TB, Xiao R, Anderson S et al (2011) Preparation of protein samples for NMR structure, function, and small-molecule screening studies. Methods Enzymol 493:21–60CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Aramini JM, Rossi P, Huang YJ et al (2008) Solution NMR structure of the NlpC/P60 domain of lipoprotein Spr from Escherichia coli: structural evidence for a novel cysteine peptidase catalytic triad. Biochemistry 47:9715–9717CrossRefPubMedGoogle Scholar
  29. 29.
    Rossi P, Aramini JM, Xiao R et al (2009) Structural elucidation of the Cys-His-Glu-Asn proteolytic relay in the secreted CHAP domain enzyme from the human pathogen Staphylococcus saprophyticus. Proteins 74:515–519CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Linding R, Jensen LJ, Diella F et al (2003) Protein disorder prediction: implications for structural proteomics. Structure 11:1453–1459CrossRefPubMedGoogle Scholar
  31. 31.
    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 337:635–645CrossRefPubMedGoogle Scholar
  32. 32.
    Cheng JSM, Baldi P (2005) Accurate prediction of protein disordered regions by mining protein structure data. Data Min Knowl Discov 11:213–222CrossRefGoogle Scholar
  33. 33.
    Prilusky J, Felder CE, Zeev-Ben-Mordehai T et al (2005) FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics 21:3435–3438CrossRefPubMedGoogle Scholar
  34. 34.
    Linding R, Russell RB, Neduva V et al (2003) GlobPlot: Exploring protein sequences for globularity and disorder. Nucleic Acids Res 31:3701–3708CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Dosztanyi Z, Csizmok V, Tompa P et al (2005) The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J Mol Biol 347:827–839CrossRefPubMedGoogle Scholar
  36. 36.
    Yang ZR, Thomson R, McNeil P et al (2005) RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 21:3369–3376CrossRefPubMedGoogle Scholar
  37. 37.
    Vucetic S, Brown CJ, Dunker AK et al (2003) Flavors of protein disorder. Proteins 52:573–584CrossRefPubMedGoogle Scholar
  38. 38.
    Lupas A, Van Dyke M, Stock J (1991) Predicting coiled coils from protein sequences. Science 252:1162–1164CrossRefPubMedGoogle Scholar
  39. 39.
    Romero P, Obradovic Z, Li X et al (2001) Sequence complexity of disordered protein. Proteins 42:38–48CrossRefPubMedGoogle Scholar
  40. 40.
    Romero P, Obradovic Z, Dunker AK (1999) Folding minimal sequences: the lower bound for sequence complexity of globular proteins. FEBS Lett 462:363–367CrossRefPubMedGoogle Scholar
  41. 41.
    Weathers EA, Paulaitis ME, Woolf TB et al (2007) Insights into protein structure and function from disorder-complexity space. Proteins 66:16–28CrossRefPubMedGoogle Scholar
  42. 42.
    Finn RD, Mistry J, Tate J et al (2010) The Pfam protein families database. Nucleic Acids Res 38:D211–D222CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  • Yuanpeng Janet Huang
    • 1
  • Thomas B. Acton
    • 1
  • Gaetano T. Montelione
    • 1
  1. 1.Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics ConsortiumRutgers UniversityPiscatawayUSA

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