ASEB: a web server for KAT-specific acetylation site prediction

Abstract

Protein lysine acetylation plays an important role in the normal functioning of cells, including gene expression regulation, protein stability and metabolism regulation. Although large amounts of lysine acetylation sites have been identified via large-scale mass spectrometry or traditional experimental methods, the lysine (K)-acetyl-transferase (KAT) responsible for the acetylation of a given protein or lysine site remains largely unknown due to the experimental limitations of KAT substrate identification. Hence, the in silico prediction of KAT-specific acetylation sites may provide direction for further experiments. In our previous study, we developed the acetylation set enrichment based (ASEB) computer program to predict which KAT-families are responsible for the acetylation of a given protein or lysine site. In this article, we provide KAT-specific acetylation site prediction as a web service. This web server not only provides the online tool and R package for the method in our previous study, but several useful services are also included, such as the integration of protein–protein interaction information to enhance prediction accuracy. This web server can be freely accessed at http://cmbi.bjmu.edu.cn/huac.

SIRT1 contributes to aldose reductase expression through modulating NFAT5 under osmotic stress: In vitro and in silico insights

“…10 different algorithms were used; KacePred [48], PAIL [49], ASEB [50,51], Predmod [52], BRABSB-PHKA [53], PSKAcePred [54], PLMLA [55], PHOSIDA [56], EnsemblePail [57] and Lys Acet [58], which are, to our knowledge, the most frequently and widely used/cited methods for prediction of lysine acetylation sites. Threshold values were taken as stringent as possible for all predictions (Supplementary Table 1).…”

In silico prediction and characterization of protein post-translational modifications

“…Reproduced from Figure 1 (panel A), Figure 3 (panel B) and Figure 4 (panel C) in [119]. M A N U S C R I P T phosphorylation general and kinase-specific sites [7] kinase-specificity with indirect relationship [8] phosphorylation and conformational flexibility [9] association patterns around phosphorylation sites [10] random forests [11] relevance units machines [12] sequence with feature selection [13] in eukaryotes [14] in bacteria [15] lysine acetylation support vector machines [16,17] bi-relative adapted binomial score Bayes feature representation [18] logistic regression classifiers [19] solvent accessibility and acetylation [20] PTM crosstalk [21] combination of multiple features [22] position-specificity [23] sequence and functional features [24] characterization of KAT acetylation sites [25] proteome-wide prediction [26] lysine acetylation in context [27] prediction of KAT acetylation sites [28] lysine methylation combination of multiple features [22] conditional random field [29] composition of K-spaced amino acid pairs [30] glycosylation O-glycosylation [31] N-, O-and C-glycosites in eukaryotes [32] in evolutionarily distant eukaryotes [33] O-GlcNAc [34] amidation feature selection [35] -carboxylation random forest [36] scoring matrices based on evolutionary information [37] disulfides feature selection [38] S-glutathionylation protein sequence …”

Aggregate-Prone R120GCRYAB Triggers Multifaceted Modifications of the Thioredoxin System

“…Bioinformatic analysis of TrxR1 amino-acid sequence suggested the existence of potential sites for both phosphorylation and acetylation PTMs ( Supplementary Fig. S2 and Supplementary Table S2) (23,62). Therefore, we sought to determine whether TrxR1 PTMs could account for TrxR1 enhanced activity.…”