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Identification of Lysine Acetylation Sites on MERS-CoV Replicase pp1ab.

ABSTRACT: MERS is a life-threatening disease and MERS-CoV has the potential to cause the next pandemic. Protein acetylation is known to play a crucial role in host response to viral infection. Acetylation of viral proteins encoded by other RNA viruses have been reported to affect viral replication. It is therefore of interest to see whether MERS-CoV proteins are also acetylated. Viral proteins obtained from infected cells were trypsin-digested into peptides. Acetylated peptides were enriched by immunoprecipitation and subject to nano-LC-Orbitrap analysis. Bioinformatic analysis was performed to assess the conservation level of identified acetylation sites and to predict the upstream regulatory factors. A total of 12 acetylation sites were identified from 7 peptides, which all belong to the replicase polyprotein pp1ab. All identified acetylation sites were found to be highly conserved across MERS-CoV sequences in NCBI database. Upstream factors, including deacetylases of the SIRT1 and HDAC families as well as acetyltransferases of the TIP60 family, were predicted to be responsible for regulating the acetylation events identified. Western blotting confirms that acetylation events indeed occur on pp1ab protein by expressing NSP4 in HEK293 cells. Acetylation events on MERS-CoV viral protein pp1ab were identified for the first time, which indicate that MERS-CoV might use the host acetylation machinery to regulate its enzyme activity and to achieve optimal replication. Upstream factors were predicted, which might facilitate further analysis of the regulatory mechanism of MERS-CoV replication.

SUBMITTER: Zhu L

PROVIDER: S-EPMC7833549 | biostudies-literature |

REPOSITORIES: biostudies-literature

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A deep learning method to more accurately recall known lysine acetylation sites.

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Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.

Project description:BackgroundAs a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet.MethodsIn this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm.ResultsExperimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites.ConclusionA user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.

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