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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Schmidler, SC; Lucas, JE; Oas, TG

Published Date

  • December 2007

Published In

Volume / Issue

  • 14 / 10

Start / End Page

  • 1287 - 1310

PubMed ID

  • 18047425

Pubmed Central ID

  • 18047425

Electronic International Standard Serial Number (EISSN)

  • 1557-8666

International Standard Serial Number (ISSN)

  • 1066-5277

Digital Object Identifier (DOI)

  • 10.1089/cmb.2007.0008

Language

  • eng