Methodology article

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Mikael Bodén^1*, Zheng Yuan² and Timothy L Bailey²

• * Corresponding author: Mikael Bodén mikael@itee.uq.edu.au

Author Affiliations

¹ School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, St Lucia, Australia

² Institute of Molecular Bioscience, The University of Queensland, QLD 4072, St Lucia, Australia

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BMC Bioinformatics 2006, 7:68 doi:10.1186/1471-2105-7-68

Published: 14 February 2006

Abstract

Background

The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models.

Results

Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues.

Conclusion

Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.