Bayesian segmentation of protein secondary structure.

Published

Journal Article

We present a novel method for predicting the secondary structure of a protein from its amino acid sequence. Most existing methods predict each position in turn based on a local window of residues, sliding this window along the length of the sequence. In contrast, we develop a probabilistic model of protein sequence/structure relationships in terms of structural segments, and formulate secondary structure prediction as a general Bayesian inference problem. A distinctive feature of our approach is the ability to develop explicit probabilistic models for alpha-helices, beta-strands, and other classes of secondary structure, incorporating experimentally and empirically observed aspects of protein structure such as helical capping signals, side chain correlations, and segment length distributions. Our model is Markovian in the segments, permitting efficient exact calculation of the posterior probability distribution over all possible segmentations of the sequence using dynamic programming. The optimal segmentation is computed and compared to a predictor based on marginal posterior modes, and the latter is shown to provide significant improvement in predictive accuracy. The marginalization procedure provides exact secondary structure probabilities at each sequence position, which are shown to be reliable estimates of prediction uncertainty. We apply this model to a database of 452 nonhomologous structures, achieving accuracies as high as the best currently available methods. We conclude by discussing an extension of this framework to model nonlocal interactions in protein structures, providing a possible direction for future improvements in secondary structure prediction accuracy.

Full Text

Duke Authors

Cited Authors

  • Schmidler, SC; Liu, JS; Brutlag, DL

Published Date

  • February 2000

Published In

Volume / Issue

  • 7 / 1-2

Start / End Page

  • 233 - 248

PubMed ID

  • 10890399

Pubmed Central ID

  • 10890399

Electronic International Standard Serial Number (EISSN)

  • 1557-8666

International Standard Serial Number (ISSN)

  • 1066-5277

Digital Object Identifier (DOI)

  • 10.1089/10665270050081496

Language

  • eng