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BMCBI
2006

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

10 years 2 months ago
Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures
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 standar...
Mikael Bodén, Zheng Yuan, Timothy L. Bailey
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where BMCBI
Authors Mikael Bodén, Zheng Yuan, Timothy L. Bailey
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