<iframe src="//www.googletagmanager.com/ns.html?id=GTM-5TSRKG" height="0" width="0" style="display: none; visibility: hidden">
Research Article
No access
Published Online: 11 December 2007

Statistical Estimation of Statistical Mechanical Models: Helix-Coil Theory and Peptide Helicity Prediction

Publication: Journal of Computational Biology
Volume 14, Issue Number 10

Abstract

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. L1-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.

Get full access to this article

View all available purchase options and get full access to this article.

Information & Authors

Information

Published In

cover image Journal of Computational Biology
Journal of Computational Biology
Volume 14Issue Number 10December 2007
Pages: 1287 - 1310
PubMed: 18047425

History

Published online: 11 December 2007
Published in print: December 2007
Published ahead of print: 29 November 2007

Permissions

Request permissions for this article.

Topics

Authors

Affiliations

Scott C. Schmidler
Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina.
Joseph E. Lucas
Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina.
Terrence G. Oas
Department of Biochemistry, Duke University Medical Center, Durham, North Carolina.

Metrics & Citations

Metrics

Citations

Export citation

Select the format you want to export the citations of this publication.

View Options

Get Access

Access content

To read the fulltext, please use one of the options below to sign in or purchase access.

Society Access

If you are a member of a society that has access to this content please log in via your society website and then return to this publication.

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

View options

PDF/EPUB

View PDF/ePub

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share on social media

Back to Top