Background: As one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins.

No abstract

Protein phosphorylation affects a multitude of cellular signaling processes. By predicting protein phosphorylation sites from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Here, we present a neural-genetic network algorithm that predicts phosphorylation sites in proteins. Aided by a genetic algorithm to optimize the weight values within the neural network ,the new algorithm has demonstrated a high accuracy of 75.1%, 82.7% and 79.2% in predicting the phosphorylated S, T and Y sites, respectively. The prediction system can be applied to other prediction tasks in the field of protein bioinformatics.