skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy

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., (13)C-(13)C 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 promisesmore » to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [2];  [2];  [1];  [1]
  1. University of Illinois, Urbana-Champaign
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1037137
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Journal of Biomolecular NMR
Additional Journal Information:
Journal Volume: 52; Journal Issue: 1; Journal ID: ISSN 0925-2738
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; ACCURACY; ALIGNMENT; AMINO ACIDS; CHEMICAL SHIFT; COMPLIANCE; PROCUREMENT; PROTEIN STRUCTURE; PROTEINS; RESIDUES; SIMULATION; SPECTRA; TARGETS; TRAINING

Citation Formats

Brothers, Michael C, Nesbitt, Anna E, Hallock, Michael J, Rupasinghe, Sanjeewa, Tang, Ming, Harris, Jason B, Baudry, Jerome Y, Schuler, Mary A, and Rienstra, Chad M. VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy. United States: N. p., 2011. Web.
Brothers, Michael C, Nesbitt, Anna E, Hallock, Michael J, Rupasinghe, Sanjeewa, Tang, Ming, Harris, Jason B, Baudry, Jerome Y, Schuler, Mary A, & Rienstra, Chad M. VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy. United States.
Brothers, Michael C, Nesbitt, Anna E, Hallock, Michael J, Rupasinghe, Sanjeewa, Tang, Ming, Harris, Jason B, Baudry, Jerome Y, Schuler, Mary A, and Rienstra, Chad M. 2011. "VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy". United States.
@article{osti_1037137,
title = {VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy},
author = {Brothers, Michael C and Nesbitt, Anna E and Hallock, Michael J and Rupasinghe, Sanjeewa and Tang, Ming and Harris, Jason B and Baudry, Jerome Y and Schuler, Mary A and Rienstra, Chad M},
abstractNote = {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., (13)C-(13)C 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.},
doi = {},
url = {https://www.osti.gov/biblio/1037137}, journal = {Journal of Biomolecular NMR},
issn = {0925-2738},
number = 1,
volume = 52,
place = {United States},
year = {Sat Jan 01 00:00:00 EST 2011},
month = {Sat Jan 01 00:00:00 EST 2011}
}