Using evolutionary noise to improve prediction of rapidly evolving targeting peptides

Targeting peptides are responsible for directing proteins to the appropriate subcellular location. As a group of biological sequences, targeting peptides are evolving at a relatively high rate and exhibit diversity. We investigate if evolutionary noise – simulated mutation at the molecular level – improves target classification for a neural network predictor. Comparison with the well-known TargetP prediction service illustrates some advantages of the approach. Specifically, classification of signal peptides, which exhibit an extremely high rate of evolution, is improved.