Predicting nucleic acid torsion angle values using artificial neural networks

Abstract

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles, chi, zeta and alpha from torsion angles delta, epsilon, beta and gamma for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for delta, epsilon, beta and gamma are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.