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Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.

Publication ,  Journal Article
Schmidler, SC; Lucas, JE; Oas, TG
Published in: J Comput Biol
December 2007

Analysis of biopolymer sequences and structures generally adopts one of two approaches: use of detailed biophysical theoretical models of the system with experimentally-determined parameters, or largely empirical statistical models obtained by extracting parameters from large datasets. In this work, we demonstrate a merger of these two approaches using Bayesian statistics. We adopt a common biophysical model for local protein folding and peptide configuration, the helix-coil model. The parameters of this model are estimated by statistical fitting to a large dataset, using prior distributions based on experimental data. L(1)-norm shrinkage priors are applied to induce sparsity among the estimated parameters, resulting in a significantly simplified model. Formal statistical procedures for evaluating support in the data for previously proposed model extensions are presented. We demonstrate the advantages of this approach including improved prediction accuracy and quantification of prediction uncertainty, and discuss opportunities for statistical design of experiments. Our approach yields a 39% improvement in mean-squared predictive error over the current best algorithm for this problem. In the process we also provide an efficient recursive algorithm for exact calculation of ensemble helicity including sidechain interactions, and derive an explicit relation between homo- and heteropolymer helix-coil theories and Markov chains and (non-standard) hidden Markov models respectively, which has not appeared in the literature previously.

Duke Scholars

Published In

J Comput Biol

DOI

ISSN

1066-5277

Publication Date

December 2007

Volume

14

Issue

10

Start / End Page

1287 / 1310

Location

United States

Related Subject Headings

  • Temperature
  • Sequence Analysis, Protein
  • Reproducibility of Results
  • Protein Structure, Secondary
  • Peptides
  • Models, Statistical
  • Models, Molecular
  • Hydrophobic and Hydrophilic Interactions
  • Hydrogen-Ion Concentration
  • Bioinformatics
 

Citation

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Chicago
ICMJE
MLA
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Schmidler, S. C., Lucas, J. E., & Oas, T. G. (2007). Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction. J Comput Biol, 14(10), 1287–1310. https://doi.org/10.1089/cmb.2007.0008
Schmidler, Scott C., Joseph E. Lucas, and Terrence G. Oas. “Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.J Comput Biol 14, no. 10 (December 2007): 1287–1310. https://doi.org/10.1089/cmb.2007.0008.
Schmidler SC, Lucas JE, Oas TG. Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction. J Comput Biol. 2007 Dec;14(10):1287–310.
Schmidler, Scott C., et al. “Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.J Comput Biol, vol. 14, no. 10, Dec. 2007, pp. 1287–310. Pubmed, doi:10.1089/cmb.2007.0008.
Schmidler SC, Lucas JE, Oas TG. Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction. J Comput Biol. 2007 Dec;14(10):1287–1310.
Journal cover image

Published In

J Comput Biol

DOI

ISSN

1066-5277

Publication Date

December 2007

Volume

14

Issue

10

Start / End Page

1287 / 1310

Location

United States

Related Subject Headings

  • Temperature
  • Sequence Analysis, Protein
  • Reproducibility of Results
  • Protein Structure, Secondary
  • Peptides
  • Models, Statistical
  • Models, Molecular
  • Hydrophobic and Hydrophilic Interactions
  • Hydrogen-Ion Concentration
  • Bioinformatics