An Intelligent System for Identifying Acetylated Lysine on Histones and Nonhistone Proteins

Lu, Cheng-Tsung; Lee, Tzong-Yi; Chen, Yu‐Ju; Chen, Yi–Ju

doi:10.1155/2014/528650

Cited by 20 publications

(13 citation statements)

References 43 publications

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“…Maximal dependence decomposition (MDD) [31] was utilized to cluster all fragment sequences into subgroups in order to detect those motifs that were statistically conserved among largescale sequence data. The clustering method was performed using MDDLogo [15], which has been demonstrated to increase the effectiveness of PTM sites identification by dividing a group of protein sequences into smaller subgroups before performing the computational identification of the PTM sites [21,[34][35][36][37][38][39][40][41][42][43][44]. In this investigation, a chi-square test χ 2 (A i , A j ) was used to iteratively evaluate the interdependence between two positions, A i and A j , which are flanking the substrate site, based on the occurrence of amino acids.…”

Section: Detection Of Motif Signatures By Maximal Dependence Decomposmentioning

confidence: 99%

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

et al. 2019

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Background: Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites. Results: The TwoSampleLogo analysis indicates that positively charged and polar amino acids surrounding glutarylated sites may be associated with the specificity in substrate site of protein glutarylation. Additionally, the chi-squared test was utilized to explore the intrinsic interdependence between two positions around glutarylation sites. Further, maximal dependence decomposition (MDD), which consists of partitioning a large-scale dataset into subgroups with statistically significant amino acid conservation, was used to capture motif signatures of glutarylation sites. We considered single features, such as amino acid composition (AAC), amino acid pair composition (AAPC), and composition of k-spaced amino acid pairs (CKSAAP), as well as the effectiveness of incorporating MDD-identified substrate motifs into an integrated prediction model. Evaluation by five-fold cross-validation showed that AAC was most effective in discriminating between glutarylation and non-glutarylation sites, according to support vector machine (SVM). Conclusions: The SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.677, a specificity of 0.619, an accuracy of 0.638, and a Matthews Correlation Coefficient (MCC) value of 0.28. Using an independent testing dataset (46 glutarylated and 92 non-glutarylated sites) obtained from the literature, we demonstrated that the integrated SVM model could improve the predictive performance effectively, yielding a balanced sensitivity and specificity of 0.652 and 0.739, respectively. This integrated SVM model has been implemented as a web-based system (MDDGlutar), which is now freely available at http://csb.cse.yzu. edu.tw/MDDGlutar/.

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Section: Detection Of Motif Signatures By Maximal Dependence Decomposmentioning

confidence: 99%

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

et al. 2019

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“…This modification is found widely in eukaryotic cells and occurs during the physiological activities at various stages (Kimura et al, 2005;Ishfaq et al, 2012;Lu et al, 2014), from regulating the function of proteins, especially in terms of protein stability (Zhou et al, 2016), to intracellular signal transduction (Batta et al, 2007;Tang et al, 2007;Spange et al, 2009), disease and other physiological processes (Batta et al, 2007;Iyer et al, 2012;Marouco et al, 2013;Ye et al, 2017). Acetylation occurs on the ε-amino group of lysine residues (Lu et al, 2014). As a highly reversible protein modification process, acetylation is regulated by the action of histone acetylase and histone deacetylase (Guarente, 2011;Qian et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The effect of acetylation on the protein stability of BmApoLp‐III in the silkworm, Bombyx mori

Yang

Zhu

Liu

et al. 2019

Insect Molecular Biology

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Acetylation is an important, reversible posttranslational modification to a protein. In a previous study, we found that there were a large number of acetylated sites in various nutrient storage proteins of the silkworm haemolymph. In this study, we confirmed that acetylation can affect the stability of nutrient storage protein Bombyx mori apolipophorin‐III (BmApoLp‐III). First, the expression of BmApoLp‐III could be upregulated when BmN cells were treated with the deacetylase inhibitor panobinostat (LBH589); similarly, the expression was downregulated when the cells were treated with the acetylase inhibitor C646. Furthermore, the increase in acetylation by LBH589 could inhibit the degradation and improve the accumulation of BmApoLp‐III in BmN cells treated with cycloheximide and MG132 respectively. Moreover, we found that an increase in acetylation could decrease the ubiquitination of BmApoLp‐III and vice versa; therefore, we predicted that acetylation could improve the stability of BmApoLp‐III by competing for ubiquitination and inhibiting the protein degradation pathway mediated by ubiquitin. Additionally, BmApoLp‐III had an antiapoptosis function that increased after LBH589 treatment, which might have been due to the improved protein stability after acetylation. These results have laid the foundation for further study on the mechanism of acetylation in regulating the storage and utilization of silkworm nutrition.

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“…Although over 58,000 acetylation sites have been characterized in prokaryotic and eukaryotic species, the regulatory HATs of most of these sites still remain to be elucidated. Previously, we and others developed about 15 computational programs to predict general acetylation sites from protein sequences, with a satisfying accuracy716171819202122232425262728293031. However, the prediction of HAT-specific acetylation sites was still unavailable until the release of ASEB1132, which clearly demonstrated that different types of HATs could modify distinct protein substrates1132.…”

Section: Discussionmentioning

confidence: 99%

“…compiled a much larger training data set with 3,600 human lysine acetylation sites from a large-scale study7, and adopted the support vector machines (SVMs) algorithm to predict acetylation sites18. To date, there have been at least a dozen of additional computational programs constructed for the accurate prediction of general lysine acetylation sites, such as LysAcet19, N-Ace20, EnsemblePail21, BPBPHKA22, PLMLA2324, PSKAcePred25, KAcePred26, LAceP27, SSPKA28, AceK29, iPTM-mLys30 and KA-predictor31. However, none of them can predict HAT-specific sites.…”

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confidence: 99%

GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences

Deng

Wang

Zhang

et al. 2016

Sci Rep

103

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Protein acetylation catalyzed by specific histone acetyltransferases (HATs) is an essential posttranslational modification (PTM) and involved in the regulation a broad spectrum of biological processes in eukaryotes. Although several ten thousands of acetylation sites have been experimentally identified, the upstream HATs for most of the sites are unclear. Thus, the identification of HAT-specific acetylation sites is fundamental for understanding the regulatory mechanisms of protein acetylation. In this work, we first collected 702 known HAT-specific acetylation sites of 205 proteins from the literature and public data resources, and a motif-based analysis demonstrated that different types of HATs exhibit similar but considerably distinct sequence preferences for substrate recognition. Using 544 human HAT-specific sites for training, we constructed a highly useful tool of GPS-PAIL for the prediction of HAT-specific sites for up to seven HATs, including CREBBP, EP300, HAT1, KAT2A, KAT2B, KAT5 and KAT8. The prediction accuracy of GPS-PAIL was critically evaluated, with a satisfying performance. Using GPS-PAIL, we also performed a large-scale prediction of potential HATs for known acetylation sites identified from highthroughput experiments in nine eukaryotes. Both online service and local packages were implemented, and GPS-PAIL is freely available at: http://pail.biocuckoo.org.As one of the most important and ubiquitous post-translational modifications (PTMs) in proteins, the lysine acetylation catalyzed by histone acetyltransferases (HATs) or lysine acetyltransferases (KATs) reversibly regulates a large number of biological processes, such as transcriptional regulation, metabolism and autophagy [1][2][3][4][5][6][7] . The dysregulation of site-specific HAT-substrate relations is frequently associated with human diseases such as cancers 2,3,8,9 . In eukaryotes, numerous HATs have been classified into three major families including p300/CBP, GCN5-related N-acetyltransferases (GNATs) and MYST proteins 1-3,10,11 . Different HATs can recognize overlapping but distinct substrates 1,11,12 . Most HATs exist in multisubunit complexes in vivo by physically interacting with non-catalytic proteins, which are also involved in recognizing substrates and synergistically determine the specificity together with HATs 2,3 . In this regard, the identification of HAT-specific acetylation sites in proteins is fundamental for understanding the molecular mechanisms and regulatory roles of lysine acetylation.Previously, systematic identification of protein acetylation sites or "acetylome" was a great challenge, due to the technical limitation 4,13 . For example, in 2006, Kim et al. used

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An Intelligent System for Identifying Acetylated Lysine on Histones and Nonhistone Proteins

Cited by 20 publications

References 43 publications

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

The effect of acetylation on the protein stability of BmApoLp‐III in the silkworm, Bombyx mori

GPS-PAIL: prediction of lysine acetyltransferase-specific modification sites from protein sequences

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