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| Title: | NN which predicts protein secondary structure |
| Author: | Burkhard Rost |
| Quote: | In: E. Fiesler and R. Beale (eds.) "Handbook of Neural Computation" New York:Oxford Univ. Press, G4.1 (1996) |
Currently, the prediction of three-dimensional (3D) protein structure from sequence alone poses insurmountable difficulties. As an intermediate step, a much simpler task has been pursued extensively: predicting 1D strings of secondary structure. Here, a composite neural network is described which predicts three secondary structure states (helix, strand, loop). The network system comprises two levels of feed-forward networks (one hidden layer each) and a final jury decision over differently trained networks. Training is done by an adaptive-like back-propagation. An important key features of the system is that the input is not only the sequence of one protein but the profile of a whole bunch of sequences of proteins which have the same 3D structure. The combination of the problem specific topology and the pre-processing of the input improve prediction accuracy from some 62% to 72%. Furthermore, the specific topology and training procedure successfully corrects for shortcomings of both simpler NN and classical methods. Over the last years, the system has been the best automatic predictor in a very competitive area of research.