Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation

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.

iAceS-Deep: Sequence-Based Identification of Acetyl Serine Sites in Proteins Using PseAAC and Deep Neural Representations

“…Xu et al [39] used RankSVM algorithm to develop an Acetylation predictor with 73.86% accuracy. Qiu et al [40] fused PseAAC and functional domain annotations to devise computational method for Acetylation prediction and their model achieve 77.10% accuracy and 0.84 AUC score. Another acetylation sites predictor was devised by Ning et al [41] using cascade SVM and two-step feature extraction which achieved 74.45% accuracy and 0.75 mAP.…”

iAcety–SmRF: Identification of Acetylation Protein by Using Statistical Moments and Random Forest

“…We begin with collection of a valid benchmark dataset for training and testing, which is the first step in the 5-step rule [1]. The dataset is collected from the well-known data repository, the UNIPROT http://www.uniprot.org, retrieved dated 8 December 2021.…”

“…It contains 2900 protein samples, of which 725 were positive denoted by S posi , and 2175 were negative denoted by S negt . Further, for the effectiveness of the proposed predictor, the set of negative data samples is equally divided in three sets, S 1 , S 2 , and S 3 as in Reference [1] such that, S i ∩ S j = φ, (i = j; , i, j = 1, 2, 3),…”

“…Among these proteins, there is a high representation of chromatin proteins and metabolic enzymes, suggesting that acetylation, in addition to digestion, has an extremely important influence on the appearance of the genetic material. Among the microbes, 90 percent of the proteins that are surrounded by the central metabolism of Salmonella Centrica are acetylated [1,2]. Acetylation also plays a prominent role in numerous important cellular processes, such as stability and localization of protein [4,5].…”

“…Reference [1] proposed a novel measurement procedure iAcet-PseFDA, a classifica- Further, acetylation sites also play an important role in regulating membrane protein functions of multiple families, as documented in Reference [5]. It is supported by examples that acetylation is significantly enhanced in membrane-binding regions, where it is often located directly in critical membrane-binding pockets ideally positioned to modulate membrane interactions.…”