Computational Systems Biology Methods and Protocols.7z

in the past several years, it remains likely that only a small portion of

the O-GlcNAcylation proteins have been discovered. Even for a

single protein, testing every serine/threonine residue for all possi-

ble modifications is typically prohibitively costly and laborious.

Therefore, computational predictive techniques can serve as tools

to generate hypotheses prior to carrying out experimental work

[5]. Unlike the high number of available predictors of other protein

posttranslational modifications, to our knowledge, there are only

six computational predictors for O-GlcNAcylation sites identifica-

tion. These predictors are listed in Table1. In this chapter, we

briefly review recent advances in the computational analysis of

Fig. 1The chemical structure of O-GlcNAc glycosylation

Table 1
A summary of O-GlcNAcylation sites prediction tools

Tool name Data sources

Positive

training data Window size

Classify

algorithms Year

YingOYang Swiss-Prot 40 – 2002

OGlcNAcScan PubMed 373 11 SVM 2011

O-GlcNAcPRED dbOGAP 373 23 SVM 2013

Wu et al. method dbOGAP, UniProtKB,

O-GlycBase,

PhosphoSiteP

375 11 SVM 2015

Kao et al. method dbOGAP, UniProtKB,

O-GlycBase

410 11 HMM, SVM 2015

PGlcS dbOGAP 373 15 SVM 2015

236 Cangzhi Jia and Yun Zuo