Abstract
Acetylation is one of post-translational modification (PTM), which often reacts with acetic acid and brings an acetyl radical to an organic compound. It is helpful to identify acetylation protein correctly for understanding the mechanism of acetylation in biological systems. Although many acetylation sites have been identified by high throughput experimental studies via mass spectrometry, there still are lots of acetylation sites need to be discovered. Computational methods have showed their power for identifying acetylation sites with informatics techniques which usually reduce experiment cost and improve the effectiveness and efficiency. In fact, if there is an approach can distinguish the acetylated proteins from the non-acetylated ones, it is no doubt a very meaningful and effective method for this issue. Here, we proposed a novel computational method for identifying acetylation proteins by extracting features from the conservation information of sequence via gray system model and KNN scores based on the information of functional domain annotation and subcellular localization. The authors have performed the 5-fold cross-validation on three datasets along with much analysis of features and the Relief feature selection algorithm. The obtained accuracies are all satisfactory, as the mean performance, the accuracy is 77.10%, the Matthew's correlation coefficient is 0.5457, and the AUC value is 0.8389. These works might provide useful insights for the related experimental validation, and further studies of other PTM process. For the convenience of related researchers, the web-server named “iACetyP” was established and is accessible at http://www.jci-bioinfo.cn/iAcetyP.
Highlights
To date, more than 450 unique protein modifications have been identified (Han et al, 2018), including phosphorylation, acetylation, ubiquitination, and sumoylation, which are regulatory mechanisms of cellular proteins with a number of biological functions, and are very important for regulating the function of many prokaryotic and eukaryotic proteins (Yang et al, 2017)
This paper proposed a new computational model for identifying potential acetylation proteins only on the basis of a query amino acid sequence by using its evolution information obtained with gray system model (Gray-PSSM) (Kaur and Raghava, 2004; Jones, 2007) and KNN scores calculated with the fuzzy distance by using its FDA and subcellular localization information
We assume that our work is to identify whether an uncharacterized amino acid sequence is acetylation protein
Summary
More than 450 unique protein modifications have been identified (Han et al, 2018), including phosphorylation, acetylation, ubiquitination, and sumoylation, which are regulatory mechanisms of cellular proteins with a number of biological functions, and are very important for regulating the function of many prokaryotic and eukaryotic proteins (Yang et al, 2017). Some studies had showed that experimental methods and computational models can be used to identify underlying PTMs sites (Hershko and Ciechanover, 1998; Haglund and Dikic, 2005; Tung and Ho, 2008; Radivojac et al, 2010), such as ubiquitination model of Radivojac et al (2010), Zhao et al (2011), and Cai et al (2012), phosphorylation model of Ingrell et al (2007), Yao et al (2012, 2015), Chen et al (2015), Li et al (2015), Trost et al (2015), and Xu et al (2015), sumoylation model of Beauclair et al (2015), Xu et al (2016), and Han et al (2018), acetylation model of Zhao et al (2010), Wang et al (2012), Hou et al (2014), and Wuyun et al (2016), and so on These researchers did make much contribution to this issue, there is still a lot of room for improving the prediction quality. In addition to some discussion of protein abovementioned features, some hypotheses for distinguishing acetylation proteins from non-acetylation ones were depicted with the aid of training dataset
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