Prediction of protein folding using the shift-learning method with a large scale neural network

The authors previously demonstrated the utility of large neural network simulations for encoding the association between protein sequence and 3D structure for a small heterologous training set of small proteins. They report the application of this approach to a selected homologous training set of eight proteins using a Cray 2 supercomputer. The large memory of this machine made it possible to configure a network with more than 0.3 million connections and 30000 neural units; a network of this size was necessary to accommodate a new training/testing set with eight proteins of up to 140 amino acid residues. This training set was constructed to investigate the performance of the neural network approach in prediction of structure within the protease class of proteins. It is shown that a neural network trained to recognize the entire sequence of a protein using the shift-learn method can retain some of the rules of protein folding in a form which allows prediction of 3D structures