Protein backbone angle prediction with machine learning approaches

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Abstract
Motivation: Protein backbone torsion angle prediction provides useful local structural information that goes beyond conventional three-state (α, β and coil) secondary structure predictions. Accurate prediction of protein backbone torsion angles will substantially improve modeling procedures for local structures of protein sequence segments, especially in modeling loop conformations that do not form regular structures as in α-helices or β-strands. Results: We have devised two novel automated methods in protein backbone conformational state prediction: one method is based on support vector machines (SVMs); the other method combines a standard feed-forward back-propagation artificial neural network (NN) with a local structure-based sequence profile database (LSBSP1). Extensive benchmark experiments demonstrate that both methods have improved the prediction accuracy rate over the previously published methods for conformation state prediction when using an alphabet of three or four states. Availability: LSBSP1 and the NN algorithm have been implemented in PrISM.1, which is available from www.columbia.edu/~ay1/. Supplementary information: Supplementary data for the SVM method can be downloaded from the Website www.cs.columbia.edu/compbio/backbone.

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