Abstract

Protein translational modification is an important regulating mechanism in the cellular activities and is also responsible for many human complex diseases, such as cancer. It is a foundation in the post-translational modification proteomics to accurately identify modified proteins or sites. A number of computational techniques have recently been developed to identify modified proteins or sites and greatly speed disclosure of hidden regulations in the cell. Here, we review in silico identifications for phosphorylation, acetylation, ubiquitination, methylation, glycosylation, S-nitrosylation, sumoylation, pupylation and palmitoylation. In addition, we summarize protein translational modification databases and discuss future direction of in silico identifications of modified proteins or sites.

Keywords: Feature selection, machine learning, neural network, post-translational modification, support vector machine.

Graphical Abstract