Protein Secondary Structure Prediction Method Based on Neural Networks

Protein secondary structure prediction remains an open and important problem in life sciences as a first step towards the crucial tertiary structure prediction. In [3], a protein secondary structure prediction algorithm called PSIPRED presents an innovative approach - feeding the neural network (NN) with a position specific scoring matrix as input data. Starting from this idea, in this paper we propose a method based on breaking down the single first level NN classifier, into three separate ones, for each of the secondary structure elements (SSE) types, in order to achieve greater generalization qualities of the first level classifying algorithm. We also introduce the use of sparsely connected feed-forward NNs, instead of the classic fully interconnected one. This network architecture gains considerable speed improvements (for both the training and the testing part of the algorithm) by omitting the most of the remote units that have the poorest influence on the selected amino acid. The prediction results are encouraging - the predictions are similar to the target PDB data and we achieve better accuracy, compared to the predictions obtained from the original PSIPRED algorithm.