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Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression

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Abstract

The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the 1H NMR and 13C chemical shifts reported for non-exchangeable protons of 187 of these RNAs. Software was developed that downloads BMRB datasets and corresponding PDB structure files, and then generates residue-specific attributes based on the calculated secondary structure. Attributes represent properties present in each sequential stretch of five adjacent residues and include variables such as nucleotide type, base-pair presence and type, and tetraloop types. Attributes and 1H and 13C NMR chemical shifts of the central nucleotide are then used as input to train a predictive model using support vector regression. These models can then be used to predict shifts for new sequences. The new software tools, available as stand-alone scripts or integrated into the NMR visualization and analysis program NMRViewJ, should facilitate NMR assignment and/or validation of RNA 1H and 13C chemical shifts. In addition, our findings enabled the re-calibration a ring-current shift model using published NMR chemical shifts and high-resolution X-ray structural data as guides.

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References

  • Aeschbacher T, Schubert M, Allain FHT (2012) A procedure to validate and correct the 13C chemical shift calibration of RNA datasets. J Biomol NMR 52:179–190

    Article  Google Scholar 

  • Aeschbacher T et al (2013) Automated and assisted RNA resonance assignment using NMR chemical shift statistics. Nucleic Acids Res 41:e172. doi:10.1093/nar/gkt665

    Article  Google Scholar 

  • Altona C, Faber DH, Westra Hoekzema AJA (2000) Double-helical DNA 1H chemical shifts: an accurate and balanced predictive scheme. Magn Reson Chem 38:95–107

    Article  Google Scholar 

  • Bartel D (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297

    Article  Google Scholar 

  • Barton S, Heng X, Johnson B, Summers M (2013) Database proton NMR chemical shifts for RNA signal assignment and validation. J Biomol NMR 55:33–46. doi:10.1007/s10858-012-9683-9

    Article  Google Scholar 

  • Bessonov S, Anokhina M, Will C, Urlaub H, Luhrmann R (2008) Isolation of an active step I spliceosome and composition of its RNP core. Nature 452:846–850. doi:10.1038/nature06842

    Article  ADS  Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics. Springer, New York

    Google Scholar 

  • Boisvert F, van Koningsbruggen S, Navascues J, Lamond A (2007) The multifunctional nucleolus. Nat Rev Mol Cell Biol 8:574–585. doi:10.1038/nrm2184

    Article  Google Scholar 

  • Bothe J, Nikolova E, Eichhorn C, Chugh J, Hansen A, Al-Hashimi H (2011) Characterizing RNA dynamics at atomic resolution using solution-state NMR spectroscopy. Nat Methods 8:919–931. doi:10.1038/nmeth.1735

    Article  Google Scholar 

  • Brodersen P, Voinnet O (2006) The diversity of RNA silencing pathways in plants. Trends Genet 22:268–280. doi:10.1016/j.tig.2006.03.003

    Article  Google Scholar 

  • Case D (1995) Calibration of ring-current effects in proteins and nucleic acids. J Biomol NMR 6:341–346

    Article  Google Scholar 

  • Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:27

    Google Scholar 

  • Cromsigt JA, Hilbers CW, Wijmenga SS (2001) Prediction of proton chemical shifts in RNA. Their use in structure refinement and validation. J Biomol NMR 21:11–29

    Article  Google Scholar 

  • Dejaegere A, Bryce RA, Case DA (1999) An empirical analysis of proton chemical shifts in nucleic acids. In: Facelli J, deDios AC (eds) Modelling NMR chemical shifts: gaining insight into structure and environment. ACS symposium series. American Chemical Society, Washington, pp 194–206

  • Doudna J, Rath V (2002) Structure and function of the eukaryotic ribosome: the next frontier. Cell 109:153–156

    Article  Google Scholar 

  • Edwards T, Klein D, Ferre-D’Amare A (2007) Riboswitches: small-molecule recognition by gene regulatory RNAs. Curr Opin Struct Biol 17:273–279. doi:10.1016/j.sbi.2007.05.004

    Article  Google Scholar 

  • Fares C, Amata I, Carlomagno T (2007) 13C-detection in RNA bases: revealing structure-chemical shift relationships. J Am Chem Soc 129:15814–15823. doi:10.1021/ja0727417

    Article  Google Scholar 

  • Fonville JM et al (2012) Chemical shifts in nucleic acids studied by density functional theory calculations and comparison with experiment. Chemistry 18:12372–12387. doi:10.1002/chem.201103593

    Article  Google Scholar 

  • Frank AT, Bae SH, Stelzer AC (2013) Prediction of RNA 1H and 13C chemical shifts: a structure based approach. J Phys Chem B 117:13497–13506. doi:10.1021/jp407254m

    Article  Google Scholar 

  • Frank A, Law S, Brooks C (2014) A simple and fast approach for predicting 1H and 13C chemical shifts: toward chemical shift-guided simulations of RNA. J Phys Chem 118:12168–12175

    Article  Google Scholar 

  • Haigh C, Mallion R (1980) Progress in NMR spectroscopy, vol 13. Pergamon, New York, pp 303–344

    Google Scholar 

  • Hamada M (2015) RNA secondary structure prediction from multi-aligned sequences. Methods Mol Biol 1269:17–38. doi:10.1007/978-1-4939-2291-8_2

    Article  Google Scholar 

  • Hassouna N, Michot B, Bachellerie J (1984) The complete nucleotide sequence of mouse 28S rRNA gene. Implications for the process of size increase of the large subunit rRNA in higher eukaryotes. Nucleic Acids Res 12:3563–3583

    Article  Google Scholar 

  • Johnson BA, Blevins RA (1994) NMRView: a computer program for the visualization and analysis of NMR data. J Biomol NMR 4:603–614

    Article  Google Scholar 

  • Kim V (2005) Small RNAs: classification, biogenesis, and function. Mol Cells 19:1–15

    Article  Google Scholar 

  • Korostelev A, Noller H (2007) The ribosome in focus: new structures bring new insights. Trends Biochem Sci 32:434–441. doi:10.1016/j.tibs.2007.08.002

    Article  Google Scholar 

  • Krahenbuhl B, Lukavsky P, Wider G (2014) Strategy for automated NMR resonance assignment of RNA: application to 48-nucleotide K10. J Biomol NMR 59:231–240. doi:10.1007/s10858-014-9841-3

    Article  Google Scholar 

  • Kwok CK, Lam SL (2013) NMR proton chemical shift prediction of T·T mismatches in B-DNA duplexes. J Magn Reson 234:184–189. doi:10.1016/j.jmr.2013.06.022

    Article  ADS  Google Scholar 

  • Lam SL (2007) DSHIFT: a web server for predicting DNA chemical shifts. Nucleic Acids Res 35:W713–W717. doi:10.1093/nar/gkm320

    Article  Google Scholar 

  • Lam SL, Lai KF, Chi LM (2007) Proton chemical shift prediction of A·A mismatches in B-DNA duplexes. J Magn Reson 187:105–111. doi:10.1016/j.jmr.2007.04.005

    Article  ADS  Google Scholar 

  • Lu X, Olson W (2008) 3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures. Nat Protoc 3:1213–1227. doi:10.1038/nprot.2008.104

    Article  Google Scholar 

  • Lu X, Olson W, Bussemaker H (2010) The RNA backbone plays a crucial role in mediating the intrinsic stability of the GpU dinucleotide platform and the GpUpA/GpA miniduplex. Nucleic Acids Res 38:4868–4876. doi:10.1093/nar/gkq155

    Article  Google Scholar 

  • Ng KS, Lam SL (2015) NMR proton chemical shift prediction of C·C mismatches in B-DNA. J Magn Reson 252:87–93. doi:10.1016/j.jmr.2015.01.005

    Article  ADS  Google Scholar 

  • Ponting C, Oliver P, Reik W (2009) Evolution and functions of long noncoding RNAs. Cell 136:629–641. doi:10.1016/j.cell.2009.02.006

    Article  Google Scholar 

  • Sahakyan AB, Vendruscolo M (2013) Analysis of the contributions of ring current and electric field effects to the chemical shifts of RNA bases. J Phys Chem B 117:1989–1998. doi:10.1021/jp3057306

    Article  Google Scholar 

  • Shen Y, Bax A (2010) SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J Biomol NMR 48:13–22. doi:10.1007/s10858-010-9433-9

    Article  Google Scholar 

  • Sripakdeevong P et al (2014) Structure determination of noncanonical RNA motifs guided by (1)H NMR chemical shifts. Nat Methods 11:413–416. doi:10.1038/nmeth.2876

    Article  Google Scholar 

  • Steitz T (2008) A structural understanding of the dynamic ribosome machine. Nat Rev Mol Cell Biol 9:242–253. doi:10.1038/nrm2352

    Article  Google Scholar 

  • Tolbert B et al (2010) Major groove width variations in RNA structures determined by NMR and impact of 13C residual chemical shift anisotropy and 1H-13C residual dipolar coupling on refinement. J Biomol NMR 47:205–219. doi:10.1007/s10858-010-9424-x

    Article  Google Scholar 

  • Ulrich E et al (2008) BioMagResBank. Nucleic Acids Res 36:D402–D408. doi:10.1093/nar/gkm957

    Article  Google Scholar 

  • van der Werf RM, Tessari M, Wijmenga SS (2013) Nucleic acid helix structure determination from NMR proton chemical shifts. J Biomol NMR 56:95–112. doi:10.1007/s10858-013-9725-y

    Article  Google Scholar 

  • Wakeman CA, Winkler WC, Dann III CE (2007) Structural features of metabolite-sensing riboswitches. Trends Biochem Sci 32:415–424. doi:10.1016/j.tibs.2007.08.005

    Article  Google Scholar 

  • Wang Y, Witten IH (2002) Modeling for optimal probability prediction. In: Proceedings of the nineteenth international conference on machine learning, 2002. Morgan Kaufmann, San Mateo, pp 650–657

  • Wang L, Eghbalnia H, Bahrami A, Markley J (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 32:13–22. doi:10.1007/s10858-005-1717-0

    Article  Google Scholar 

  • Wang B, Wang Y, Wishart D (2010) A probabilistic approach for validating protein NMR chemical shift assignments. J Biomol NMR 47:85–99. doi:10.1007/s10858-010-9407-y

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann, San Mateo

  • Wüthrich K (1995) NMR in structural biology: a collection of papers by Kurt Wüthrich. World Scientific series in 20th century chemistry, vol 5. World Scientific, Singapore, River Edge

  • Xu X, Case D (2001) Automated prediction of 15N, 13Calpha, 13Cbeta and 13C′ chemical shifts in proteins using a density functional database. J Biomol NMR 21:321–333

    Article  Google Scholar 

  • Zhang H, Neal S, Wishart D (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by Grants from the National Institute of General Medical Sciences of the National Institutes of Health (NIGMS, R01 GM42561 to MFS and P50 GM 103297 to BAJ), and JDB was supported by a NIGMS Grant for maximizing student diversity, NIGMS R25 GM 055036. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Howard Hughes Medical Institute, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA

    Joshua D. Brown & Michael F. Summers

  2. Department of Chemistry and Biochemistry, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA

    Joshua D. Brown, Michael F. Summers & Bruce A. Johnson

  3. One Moon Scientific, Inc., 839 Grant Ave., Westfield, NJ, 07090, USA

    Bruce A. Johnson

  4. CUNY Advanced Science Research Center, 85 St. Nicholas Terrace, New York, NY, 10031, USA

    Bruce A. Johnson

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  1. Joshua D. Brown
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Correspondence to Bruce A. Johnson.

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Brown, J.D., Summers, M.F. & Johnson, B.A. Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression. J Biomol NMR 63, 39–52 (2015). https://doi.org/10.1007/s10858-015-9961-4

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