Prediction of Protein Lysine Acylation by Integrating Primary Sequence Information with Multiple Functional Features

Liquid chromatography–tandem
mass spectrometry (LC–MS/MS)-based
proteomic methods have been widely used to identify lysine acylation
proteins. However, these experimental approaches often fail to detect
proteins that are in low abundance or absent in specific biological
samples. To circumvent these problems, we developed a computational
method to predict lysine acylation, including acetylation, malonylation,
succinylation, and glutarylation. The prediction algorithm integrated
flanking primary sequence determinants and evolutionary conservation
of acylated lysine as well as multiple protein functional annotation
features including gene ontology, conserved domains, and protein–protein
interactions. The inclusion of functional annotation features increases
predictive power oversimple sequence considerations for four of the
acylation species evaluated. For example, the Matthews correlation
coefficient (MCC) for the prediction of malonylation increased from
0.26 to 0.73. The performance of prediction was validated against
an independent data set for malonylation. Likewise, when tested with
independent data sets, the algorithm displayed improved sensitivity
and specificity over existing methods. Experimental validation by
Western blot experiments and LC–MS/MS detection further attested
to the performance of prediction. We then applied our algorithm on
to the mouse proteome and reported the global-scale prediction of
lysine acetylation, malonylation, succinylation, and glutarylation,
which should serve as a valuable resource for future functional studies.