Journal of Biomolecular NMR

, Volume 52, Issue 1, pp 41–56 | Cite as

VITAL NMR: using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy

  • Michael C. Brothers
  • Anna E. Nesbitt
  • Michael J. Hallock
  • Sanjeewa G. Rupasinghe
  • Ming Tang
  • Jason Harris
  • Jerome Baudry
  • Mary A. Schuler
  • Chad M. Rienstra


Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., 13C–13C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library of 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (−0.75) commensurate to the control (−0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.


Protein structure prediction Homology modeling Solid-state NMR spectroscopy TALOS database Chemical shift analysis 



The authors thank the National Institute of Health for funding through R01GM79530, R01GM75937, NRSA (F32 GM095344), the Ruth L. Kirschstein National Research Service Award to AEN and the Chemical Biology Interface Training Program (GM070421-06) to MCB and the Department of Homeland Security Fellowship Program to MCB, as well as Dr. Ying Li, Dr. Aleksandra Kijac, and Dr. Andrew Nieuwkoop for early assistance on this project.

Supplementary material

10858_2011_9576_MOESM1_ESM.pdf (289 kb)
Supplementary material 1 (PDF 288 kb)


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Michael C. Brothers
    • 1
  • Anna E. Nesbitt
    • 1
  • Michael J. Hallock
    • 1
  • Sanjeewa G. Rupasinghe
    • 2
  • Ming Tang
    • 1
  • Jason Harris
    • 3
  • Jerome Baudry
    • 3
    • 4
  • Mary A. Schuler
    • 2
    • 5
  • Chad M. Rienstra
    • 1
    • 5
    • 6
  1. 1.Department of ChemistryUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Department of Cell and Developmental BiologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.Department of Biochemistry, Cellular and Molecular BiologyUniversity of TennesseeKnoxvilleUSA
  4. 4.UT/ORNL Center for Molecular BiophysicsOak Ridge National LaboratoryOak RidgeUSA
  5. 5.Department of BiochemistryUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  6. 6.Center for Biophysics and Computational BiologyUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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