13C Chemical Shifts in Proteins: A Rich Source of Encoded Structural Information

  • Jorge A. Vila
  • Yelena A. Arnautova
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 1)


Despite the formidable progress in Nuclear Magnetic Resonance (NMR) spectroscopy, quality assessment of NMR-derived structures remains as an important problem. Thus, validation of protein structures is essential for the spectroscopists, since it could enable them to detect structural flaws and potentially guide their efforts in further refinement. Moreover, availability of accurate and efficient validation tools would help molecular biologists and computational chemists to evaluate quality of available experimental structures and to select a protein model which is the most suitable for a given scientific problem. The 13C α nuclei are ubiquitous in proteins, moreover, their shieldings are easily obtainable from NMR experiments and represent a rich source of encoded structural information that makes 13C α chemical shifts an attractive candidate for use in computational methods aimed at determination and validation of protein structures. In this chapter, the basis of a novel methodology of computing, at the quantum chemical level of theory, the 13C α shielding for the amino acid residues in proteins is described. We also identify and examine the main factors affecting the 13C α -shielding computation. Finally, we illustrate how the information encoded in the 13C chemical shifts can be used for a number of applications, viz., from protein structure prediction of both α-helical and β-sheet conformations, to determination of the fraction of the tautomeric forms of the imidazole ring of histidine in proteins as a function of pH or to accurate detection of structural flaws, at a residue-level, in NMR-determined protein models.


Nuclear Magnetic Resonance Chemical Shift Protein Data Bank Imidazole Ring Nuclear Magnetic Resonance Spectroscopy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jorge A. Vila
    • 1
    • 2
  • Yelena A. Arnautova
    • 3
  1. 1.IMASL-CONICETUniversidad Nacional de San LuisSan LuisArgentina
  2. 2.Baker Laboratory of Chemistry and Chemical BiologyCornell UniversityIthacaUSA
  3. 3.Molsoft L.L.C.San DiegoUSA

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