Peptides secondary structure prediction with neural networks: a criterion for building appropriate learning sets.

Artificial neural networks have been recently applied with success for protein secondary structure prediction. So far, one of the two main aspects on which neural net performance depends, the topology of the net, has been considered. The present work addresses the other main aspect, the building up of the learning set. We present a criterion to build up suitable learning sets based on the alpha-helix percentage. Starting from a set of several well known proteins, we formed 7 groups of proteins with similar helix percentages and we used them for the learning of the same neural net. We found that the best secondary structure prediction for each of the tested proteins (not belonging to the initial set) was the one obtained using the learning set whose helix percentage was closest to that of the tested protein. The accuracy of correct prediction of our method on three types of secondary structure (alpha-helix, beta-sheet and coil), has been compared with the accuracy of other secondary structure prediction methods.