Journal of Biomolecular NMR

, Volume 70, Issue 3, pp 141–165 | Cite as

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

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Abstract

Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25–78%, with the largest improvements observed for 1Hα and 13C′. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org.

Keywords

Chemical shift Software Intrinsically disordered proteins Random coil 

Supplementary material

10858_2018_166_MOESM1_ESM.docx (1.7 mb)
Supplementary material 1 (DOCX 1721 KB)

References

  1. Akaike H (1974) New look at statistical-model identification. IEEE Trans Autom Control AC19:716–723ADSMathSciNetCrossRefMATHGoogle Scholar
  2. Akaike H (1985) Prediction and entropy. A celebration of statistics. Atkinson ACF, SE New York, Springer, pp 1–24Google Scholar
  3. Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294:93–96ADSCrossRefGoogle Scholar
  4. Bartels C, Guntert P, Billeter M, Wuthrich K (1997) GARANT—a general algorithm for resonance assignment of multidimensional nuclear magnetic resonance spectra. J Comput Chem 18:139–149CrossRefGoogle Scholar
  5. Berjanskii MV, Wishart DS (2005) A simple method to predict protein flexibility using secondary chemical shifts. J Am Chem Soc 127:14970–14971CrossRefGoogle Scholar
  6. Bermel W et al (2013) High-dimensionality C-13 direct-detected NMR experiments for the automatic assignment of intrinsically disordered proteins. J Biomol NMR 57:353–361CrossRefGoogle Scholar
  7. Braun D, Wider G, Wuethrich K (1994) Sequence-corrected 15N “random coil” chemical shifts. J Am Chem Soc 116:8466–8469CrossRefGoogle Scholar
  8. Brutscher B et al (2015) NMR methods for the study of instrinsically disordered proteins structure, dynamics, and interactions: general overview and practical guidelines. Adv Exp Med Biol 870:49–122CrossRefGoogle Scholar
  9. Bundi A, Wüthrich K (1979) 1H-nmr parameters of the common amino acid residues measured in aqueous solutions of the linear tetrapeptides H-Gly-Gly-X-L-Ala-OH. Biopolymers 18:285–297CrossRefGoogle Scholar
  10. Burley SK (2000) An overview of structural genomics. Nat Struct Biol 7:932–934CrossRefGoogle Scholar
  11. Camilloni C, De Simone A, Vranken WF, Vendruscolo M (2012) Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts. Biochemistry 51:2224–2231CrossRefGoogle Scholar
  12. Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acid Sci USA 104:9615–9620ADSCrossRefGoogle Scholar
  13. Chandonia J-M, Brenner SE (2006) The impact of structural genomics: expectations and outcomes. Science 311:347–351ADSCrossRefGoogle Scholar
  14. Chen TC, Hsiao CL, Huang SJ, Huang JR (2016) The nearest-neighbor effect on random-coil nmr chemical shifts demonstrated using a low-complexity amino-acid sequence. Protein Pept Lett 23:967–975CrossRefGoogle Scholar
  15. Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302CrossRefGoogle Scholar
  16. De Simone A et al (2009) Accurate random coil chemical shifts from an analysis of loop regions in native states of proteins. J Am Chem Soc 131:16332–16333CrossRefGoogle Scholar
  17. Dunker AK et al (2002) Intrinsic disorder and protein function. Biochemistry 41:6573–6582CrossRefGoogle Scholar
  18. Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6:197–208CrossRefGoogle Scholar
  19. Eliezer D et al (2005) Residual structure in the repeat domain of tau: echoes of microtubule binding and paired helical filament formation. Biochemistry 44:1026–1036CrossRefGoogle Scholar
  20. Felli IC, Pierattelli R (2012) Recent progress in NMR spectroscopy: toward the study of intrinsically disordered proteins of increasing size and complexity. IUBMB Life 64:473–481CrossRefGoogle Scholar
  21. Georgiev AG (2009) Interpretable numerical descriptors of amino acid space. J Comput Biol 16:703–723CrossRefGoogle Scholar
  22. Han B, Liu YF, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50:43–57CrossRefGoogle Scholar
  23. Hatzopoulos GN et al (2013) Structural analysis of the G-box domain of the microcephaly protein CPAP suggests a role in centriole architecture. Structure 21:2069–2077CrossRefGoogle Scholar
  24. Isaksson L et al (2013) Highly efficient NMR Assignment of intrinsically disordered proteins: application to B- and T cell receptor domains. PLoS ONE 8:e62947ADSCrossRefGoogle Scholar
  25. Jung YS, Zweckstetter M (2004) Mars—robust automatic backbone assignment of proteins. J Biomol NMR 30:11–23CrossRefGoogle Scholar
  26. Kjaergaard M, Poulsen FM (2011) Sequence correction of random coil chemical shifts: correlation between neighbor correction factors and changes in the Ramachandran distribution. J Biomol NMR 50:157–165CrossRefGoogle Scholar
  27. Kjaergaard M, Poulsen FM (2012) Disordered proteins studied by chemical shifts. Prog Nucl Magn Reson Spectrosc 60:42–51CrossRefGoogle Scholar
  28. Kjaergaard M et al (2010) Temperature-dependent structural changes in intrinsically disordered proteins: formation of alpha-helices or loss of polyproline II? Protein Sci 19:1555–1564CrossRefGoogle Scholar
  29. Kjaergaard M, Brander S, Poulsen FM (2011) Random coil chemical shift for intrinsically disordered proteins: effects of temperature and pH. J Biomol NMR 49:139–149CrossRefGoogle Scholar
  30. Kohlhoff KJ et al (2009) Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 131:13894–13895CrossRefGoogle Scholar
  31. Kragelj J, Ozenne V, Blackledge M, Jensen MR (2013) Conformational propensities of intrinsically disordered proteins from NMR chemical shifts. Chemphyschem 14:3034–3045CrossRefGoogle Scholar
  32. Lee W, Tonelli M, Markley JL (2015) NMRFAM-SPARKY: enhanced software for biomolecular NMR spectroscopy. Bioinformatics 31:1325–1327CrossRefGoogle Scholar
  33. Marsh JA, Singh VK, Jia Z, Forman-Kay JD (2006) Sensitivity of secondary structure propensities to sequence differences between alpha- and gamma-synuclein: implications for fibrillation. Protein Sci 15:2795–2804CrossRefGoogle Scholar
  34. Meiler J (2003) PROSHIFT: Protein chemical shift prediction using artificial neural networks. J Biomol NMR 26:25–37CrossRefGoogle Scholar
  35. Merutka G, Dyson HJ, Wright PE (1995) ‘Random coil’ 1H chemical shifts obtained as a function of temperature and trifluoroethanol concentration for the peptide series GGXGG. J Biomol NMR 5:14–24CrossRefGoogle Scholar
  36. Modig K et al (2007) Detection of initiation sites in protein folding of the four helix bundle ACBP by chemical shift analysis. FEBS Lett 581:4965–4971CrossRefGoogle Scholar
  37. Montelione GT et al (2000) Protein NMR spectroscopy in structural genomics. Nat Struct Biol 7:982–985CrossRefGoogle Scholar
  38. Moseley HNB, Monleon D, Montelione GT (2001) Automatic determination of protein backbone resonance assignments from triple resonance nuclear magnetic resonance data. Nucl Magn Reson Biol Macromol Pt B 339:91–108CrossRefGoogle Scholar
  39. Mukrasch MD et al (2005) Sites of tau important for aggregation populate (beta)-structure and bind to microtubules and polyanions. J Biol Chem 280:24978–24986CrossRefGoogle Scholar
  40. Neal S, Nip AM, Zhang HY, Wishart DS (2003) Rapid and accurate calculation of protein H-1, C-13 and N-15 chemical shifts. J Biomol NMR 26:215–240CrossRefGoogle Scholar
  41. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Molec Biol 48:443–453CrossRefGoogle Scholar
  42. Nielsen JT, Mulder FAA (2016) There is diversity in disorder—“In all chaos there is a cosmos, in all disorder a secret order”. Front Mol Biosci 3:4CrossRefGoogle Scholar
  43. Nielsen JT, Nielsen NC (2014) VirtualSpectrum, a tool for simulating peak list for multi-dimensional NMR spectra. J Biomol NMR 60:51–66CrossRefGoogle Scholar
  44. Nielsen JT, Eghbalnia HR, Nielsen NC (2012) Chemical shift prediction for protein structure calculation and quality assessment using an optimally parameterized force field. Progr Nucl Magn Reson Spectrosc 60:1–28CrossRefGoogle Scholar
  45. Nielsen JT et al (2016) In situ high-resolution structure of the baseplate antenna complex in Chlorobaculum tepidum. Nat Commun 7:12454ADSCrossRefGoogle Scholar
  46. Oezguen N et al (2002) Automated assignment and 3D structure calculations using combinations of 2D homonuclear and 3D heteronuclear NOESY spectra. J Biomol NMR 22:249–263CrossRefGoogle Scholar
  47. Perez Y, Gairi M, Pons M, Bernado P (2009) Structural characterization of the natively unfolded N-terminal domain of human c-Src kinase: insights into the role of phosphorylation of the unique domain. J Mol Biol 391:136–148CrossRefGoogle Scholar
  48. Piai A et al (2014) “CON-CON’’ assignment strategy for highly flexible intrinsically disordered proteins. J Biomol NMR 60:209–218CrossRefGoogle Scholar
  49. Piai A et al (2016) Amino acid recognition for automatic resonance assignment of intrinsically disordered proteins. J Biomol NMR 64:239–253CrossRefGoogle Scholar
  50. Platzer G, Okon M, McIntosh LP (2014) pH-dependent random coil (1)H, (13)C, and (15)N chemical shifts of the ionizable amino acids: a guide for protein pK a measurements. J Biomol NMR 60:109–129CrossRefGoogle Scholar
  51. Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99CrossRefGoogle Scholar
  52. Richarz R, Wüthrich K (1978) Carbon-13 NMR chemical shifts of the common amino acid residues measured in aqueous solutions of the linear tetrapeptides H-Gly-Gly-X-L-Ala-OH. Biopolymers 17:2133–2141CrossRefGoogle Scholar
  53. Romero P et al (2001) Sequence complexity of disordered protein. Proteins 42:38–48CrossRefGoogle Scholar
  54. Rosato A et al (2012) Blind testing of routine, fully automated determination of protein structures from NMR data. Structure 20:227–236CrossRefGoogle Scholar
  55. Schmidt E, Guntert P (2012) A new algorithm for reliable and general NMR resonance assignment. J Am Chem Soc 134:12817–12829CrossRefGoogle Scholar
  56. Schwarzinger S et al (2000) Random coil chemical shifts in acidic 8 M urea: implementation of random coil shift data in NMRView. J Biomol NMR 18:43–48CrossRefGoogle Scholar
  57. Schwarzinger S et al (2001) Sequence-dependent correction of random coil NMR chemical shifts. J Am Chem Soc 123:2970–2978CrossRefGoogle Scholar
  58. Shen Y, Bax A (2007) Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J Biomol NMR 38:289–302CrossRefGoogle Scholar
  59. Shen Y et al (2008) Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acid Sci USA 105:4685–4690ADSCrossRefGoogle Scholar
  60. Shen Y, Delaglio F, Cornilescu G, Bax A (2009) TALOS plus: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44:213–223CrossRefGoogle Scholar
  61. Simon M, Hancock JM (2009) Tandem and cryptic amino acid repeats accumulate in disordered regions of proteins. Gen Biol 10:R59-R59CrossRefGoogle Scholar
  62. Simons KT, Strauss C, Baker D (2001) Prospects for ab initio protein structural genomics. J Mol Biol 306:1191–1199CrossRefGoogle Scholar
  63. Singarapu KK et al (2011) Structural characterization of Hsp12, the heat shock protein from Saccharomyces cerevisiae, in aqueous solution where it is intrinsically disordered and in detergent micelles where it is locally alpha-helical. J Biol Chem 286:43447–43453CrossRefGoogle Scholar
  64. Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and C. alpha. and C. beta. 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113:5490–5492CrossRefGoogle Scholar
  65. Stone M (1977) Asymptotics for and against cross-validation. Biometrika 64:29–35MathSciNetCrossRefMATHGoogle Scholar
  66. Tamiola K, Mulder FAA (2011) ncIDP-assign: a SPARKY extension for the effective NMR assignment of intrinsically disordered proteins. Bioinformatics 27:1039–1040CrossRefGoogle Scholar
  67. Tamiola K, Mulder FAA (2012) Using NMR chemical shifts to calculate the propensity for structural order and disorder in proteins. Biochem Soc Trans 40:1014–1020CrossRefGoogle Scholar
  68. Tamiola K, Acar B, Mulder FAA (2010) Sequence-specific random coil chemical shifts of intrinsically disordered proteins. J Am Chem Soc 132:18000–18003CrossRefGoogle Scholar
  69. Tamiola K, Scheek RM, Meulen P, Mulder FAA (2018) PepKalc-scalable and comprehensive calculation of electrostatic interactions in random coil polypeptides. Bioinformatics.  https://doi.org/10.1093/bioinformatics/bty033 Google Scholar
  70. Theil H, Theil H (1971) Principles of econometricsGoogle Scholar
  71. Ting D et al (2010) Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model. PLoS Comput Biol 6:e1000763MathSciNetCrossRefGoogle Scholar
  72. van der Lee R et al (2014) Classification of intrinsically disordered regions and proteins. Chem Rev 114:6589–6631CrossRefGoogle Scholar
  73. Verdegem D, Dijkstra K, Hanoulle X, Lippens G (2008) Graphical interpretation of Boolean operators for protein NMR assignments. J Biomol NMR 42:11–21CrossRefGoogle Scholar
  74. Wang G, Dunbrack RL Jr (2003) PISCES: a protein sequence culling server. Bioinformatics 19:1589–1591CrossRefGoogle Scholar
  75. Wang Y, Jardetzky O (2002) Investigation of the neighboring residue effects on protein chemical shifts. J Am Chem Soc 124:14075–14084CrossRefGoogle Scholar
  76. Wang L, Eghbalnia HR, Bahrami A, Markley JL (2005) Linear analysis of carbon-13 chemical shift differences and its application to the detection and correction of errors in referencing and spin system identifications. J Biomol NMR 32:13–22CrossRefGoogle Scholar
  77. Ward JJ et al (2004) Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol 337:635–645CrossRefGoogle Scholar
  78. Wilk MB, Gnanadesikan R (1968) Probability plotting methods for the analysis of data. Biometrika 55:1–17Google Scholar
  79. Williamson MP (1990) Secondary-structure dependent chemical shifts in proteins. Biopolymers 29:1423–1431CrossRefGoogle Scholar
  80. Williamson MP, Craven CJ (2009) Automated protein structure calculation from NMR data. J Biomol NMR 43:131–143CrossRefGoogle Scholar
  81. Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222:311–333CrossRefGoogle Scholar
  82. Wishart DS et al (1995) 1H, 13C and 15N random coil NMR chemical shifts of the common amino acids. I. Investigations of nearest-neighbor effects. J Biomol NMR 5:67–81CrossRefGoogle Scholar
  83. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52CrossRefGoogle Scholar
  84. Wright PE, Dyson HJ (2015) Intrinsically disordered proteins in cellular signalling and regulation. Nat Rev Mol Cell Biol 16:18–29CrossRefGoogle Scholar
  85. Zawadzka-Kazimierczuk A, Kozminski W, Billeter M (2012) TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra. J Biomol NMR 54:81–95CrossRefGoogle Scholar
  86. Zhang HY, Neal S, Wishart DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195CrossRefGoogle Scholar
  87. Zhang ZY, Porter J, Tripsianes K, Lange OF (2014) Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta. J Biomol NMR 59:135–145CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Interdisciplinary Nanoscience Center (iNANO)Aarhus UniversityAarhus CDenmark
  2. 2.Department of ChemistryAarhus UniversityAarhus CDenmark

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