Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 41-56 |
| Number of pages | 16 |
| Journal | Journal of Biomolecular NMR |
| Volume | 52 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2012 |
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Keywords
- Chemical shift analysis
- Homology modeling
- Protein structure prediction
- Solid-state NMR spectroscopy
- TALOS database
ASJC Scopus subject areas
- Biochemistry
- Spectroscopy
Cite this
VITAL NMR : Using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy. / Brothers, Michael C.; Nesbitt, Anna E.; Hallock, Michael J.; Rupasinghe, Sanjeewa G.; Tang, Ming; Harris, Jason; Baudry, Jerome; Schuler, Mary A.; Rienstra, Chad M.
In: Journal of Biomolecular NMR, Vol. 52, No. 1, 01.01.2012, p. 41-56.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - VITAL NMR
T2 - Using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy
AU - Brothers, Michael C.
AU - Nesbitt, Anna E.
AU - Hallock, Michael J.
AU - Rupasinghe, Sanjeewa G.
AU - Tang, Ming
AU - Harris, Jason
AU - Baudry, Jerome
AU - Schuler, Mary A.
AU - Rienstra, Chad M.
PY - 2012/1/1
Y1 - 2012/1/1
N2 - 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.
AB - 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.
KW - Chemical shift analysis
KW - Homology modeling
KW - Protein structure prediction
KW - Solid-state NMR spectroscopy
KW - TALOS database
UR - http://www.scopus.com/inward/record.url?scp=84856533659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856533659&partnerID=8YFLogxK
U2 - 10.1007/s10858-011-9576-3
DO - 10.1007/s10858-011-9576-3
M3 - Article
C2 - 22183804
AN - SCOPUS:84856533659
VL - 52
SP - 41
EP - 56
JO - Journal of Biomolecular NMR
JF - Journal of Biomolecular NMR
SN - 0925-2738
IS - 1
ER -