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

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 pre...

• Type: Journal
• Year: 2007
• Where: JCB
• Authors: Scott C. Schmidler, Joseph E. Lucas, Terrence G. Oas
• Keywords: JCB 2007, Large Dataset, Model, Parameters