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.


CONCLUSIONS
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.
School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, St Lucia, Australia. mikael@itee.uq.edu.au
Mikael
M
Bodén
Zheng
Z
Yuan
Timothy L
TL
Bailey
D000465:Algorithms; D003198:Computer Simulation; D003461:Crystallography / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000648:ultrastructure; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins