Post-translational
modifications
(PTMs), also called as
covalent modifications, are chemical modifying
processes of proteins after their translation.
By proteolytic cleavage of peptide chains,
by adding various functional groups (e.g.
phosphorylation) to individual amino acids,
or by altering the chemical properties of
amino acid residues (e.g. citrullination),
various PTMs create or disrupt covalent bonds
to change structures, localizations and functions
of proteins significantly, play essential
roles in almost all of cellular
signaling pathways & networks,
and determine the cellular dynamics and plasticity.
Although more than 350 types of PTMs have
been discovered, only a few of them have been
well-characterized due to the lack of sufficient
data for analyses. Experimental identification
of PTM substrates with their sites is labor-intensive
and often limited by the availability and
optimization of enzymatic reaction. In
silico prediction could be a promising
strategy to conduct preliminary analyses and
greatly reduce the number of potential targets
that need further in vivo or in
vitro confirmation.
Previously, several
types of PTMs have been investigated using
computational approaches, e.g. phosphorylation,
glycosylation, sulfation and myristoylation,
etc. However, the prediction performances
of these programs still remain to be improved.
The Cuckoo Workgroup focused on developing
more rigorous computational models and designing
more efficient algorithms to enhance the research
of PTMs. Besides the well-know PTM of phosphorylation,
we also considered several other new PTMs,
including sumoylation, palmitoylation and
Lysine/Arginine methylation, etc. We developed
several easy-to-use online web tools and downloadable
softwares. For example, we constructed GPS
and PPSP
for prediction of phosphorylation sites, based
on GPS and Bayesian Decision Theory algorithms,
respectively. And we designed the CSS-Palm
to predict the palmitoylation sites. Also,
we developed the online tool of MeMo
to predict Lysine/Arginine methylation sites,
with SVMs algorithm. Moreover, we constructed
an online tool of SUMOsp
to predict sumoylation sites, mainly with
the GPS algorithm. We also surveyed the the
functional diversity of SUMO substrates, and
carried out large-scale prediction of Protein
Kinase A (PKA) specific substrates. More analyses
will be available in the near future.
Our aim is to develop
novel algorithms and computational softwares
for understanding the temporally and spatially
regulatory roles of post-translational modifications
involved in cellular signaling pathways and
networks. We believe our and others computational
studies together with experimental identifications
will propel the research of PTMs into a newphase.
