Background: Cysteine S-sulfenylation is a major type of dynamic post-translational
modification of the protein that plays an important role in regulating many biological processes in
both of prokaryotic and eukaryotic species. To understand the function of S-sulfenylated proteins,
identification of S-sulfenylation sites is an essential step. Due to numerous restrictions of experimental
methods, computational prediction of the potential S-sulfenylation sites becomes popular. In
this review, we discuss the recent development and challenges in protein S-sulfenylation site prediction
from the available datasets, algorithms and accessible services. We also demonstrate the
encountered limitation and future perspective of the computational prediction tools.
Conclusion: The development of S-sulfenylation site prediction and their application is an emerging
field of protein bioinformatics research. Accurate predictors are expected to identify general
and species-specific S-sulfenylation sites when more experimental annotation data are available.
Combining experimental and computational technologies will definitely accelerate an understanding
of protein S-sulfenylation, discovering regulatory networks in living organisms.
Keywords: S-sulfenylation site, statistical learning, feature representation, tool development, computational prediction tools,
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