Prediction of Helix, Strand Segments from Primary Protein Sequences by a Set of Neural Networks

Abstract

In prediction of secondary structure of proteins there are always some suspected segments. These suspected segments confuse people and lower the accuracy of prediction methods. To deal with this problem, a set of neural networks (NNs) are built based on helix, strand and coil segments selected from PDB. The test performance of these NNs on training data is perfect without surprise. However the prediction on test data is not good enough because the training data are lake of great representativeness. The results support the fact that closer neighbor vectors have the similar outputs of NNs. One can improve representativeness of training data without enlarging data scale as long as select less data from dense region and more from sparse region on condition that distribution of sample data has been known.