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.
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Published In
Journal of Computational Biology
Volume 14 • Issue Number 10 • December 2007
Pages: 1287 - 1310
PubMed: 18047425
Copyright
Mary Ann Liebert, Inc.
History
Published online: 11 December 2007
Published in print: December 2007
Published ahead of print: 29 November 2007
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