Bioinformatics Advance Access originally published online on February 26, 2004
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Bioinformatics 20(10) © Oxford University Press 2004; all rights reserved.
Protein backbone angle prediction with machine learning approaches
1 Department of Computer Science, 2 Department of Pharmacology, 3 Columbia Genome Center and 4 Center for Computational Biology and Bioinformatics, Columbia University, West 168th Street, PH 7 W Room 318, New York, NY 10032, USA
Received on August 14, 2003; revised on December 19, 2003; accepted on January 5, 2004
Advance Access Publication February 26, 2004
Motivation: Protein backbone torsion angle prediction provides useful local structural information that goes beyond conventional three-state (
, ß 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 -helices or ß-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.
Contact: ay1{at}columbia.edu; cleslie{at}cs.cloumbia.edu
* To whom correspondence should be addressed.
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