Identification of mouse hepatitis coronavirus A59 nucleocapsid protein phosphorylation sites

AbstractProtein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.

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Analysis of Flagellar Phosphoproteins from Chlamydomonas reinhardtii

Eukaryotic Cell ◽

10.1128/ec.00067-09 ◽

2009 ◽

Vol 8 (7) ◽

pp. 922-932 ◽

Cited By ~ 37

Author(s):

Jens Boesger ◽

Volker Wagner ◽

Wolfram Weisheit ◽

Maria Mittag

Keyword(s):

Chlamydomonas Reinhardtii ◽

Protein Phosphorylation ◽

Proteome Analysis ◽

Physiological Status ◽

Phosphorylation Sites ◽

Cell Organelles ◽

Flagellar Proteins ◽

Reversible Protein Phosphorylation ◽

Cilia And Flagella

ABSTRACT Cilia and flagella are cell organelles that are highly conserved throughout evolution. For many years, the green biflagellate alga Chlamydomonas reinhardtii has served as a model for examination of the structure and function of its flagella, which are similar to certain mammalian cilia. Proteome analysis revealed the presence of several kinases and protein phosphatases in these organelles. Reversible protein phosphorylation can control ciliary beating, motility, signaling, length, and assembly. Despite the importance of this posttranslational modification, the identities of many ciliary phosphoproteins and knowledge about their in vivo phosphorylation sites are still missing. Here we used immobilized metal affinity chromatography to enrich phosphopeptides from purified flagella and analyzed them by mass spectrometry. One hundred forty-one phosphorylated peptides were identified, belonging to 32 flagellar proteins. Thereby, 126 in vivo phosphorylation sites were determined. The flagellar phosphoproteome includes different structural and motor proteins, kinases, proteins with protein interaction domains, and many proteins whose functions are still unknown. In several cases, a dynamic phosphorylation pattern and clustering of phosphorylation sites were found, indicating a complex physiological status and specific control by reversible protein phosphorylation in the flagellum.

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High-accuracy identification and bioinformatic analysis of in vivo protein phosphorylation sites in yeast

PROTEOMICS ◽

10.1002/pmic.200900144 ◽

2009 ◽

Vol 9 (20) ◽

pp. 4642-4652 ◽

Cited By ~ 90

Author(s):

Florian Gnad ◽

Lyris M. F. de Godoy ◽

Jürgen Cox ◽

Nadin Neuhauser ◽

Shubin Ren ◽

...

Keyword(s):

Protein Phosphorylation ◽

Bioinformatic Analysis ◽

High Accuracy ◽

Phosphorylation Sites

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Identification of Protein Phosphorylation Sites by Advanced LC-ESI-MS/MS Methods

Post-Translational Modification of Proteins - Methods in Molecular Biology ◽

10.1007/978-1-4939-9055-9_11 ◽

2019 ◽

pp. 163-178

Author(s):

Christof Lenz

Keyword(s):

Protein Phosphorylation ◽

Phosphorylation Sites ◽

Esi Ms

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GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System

International Journal of Molecular Sciences ◽

10.3390/ijms21217891 ◽

2020 ◽

Vol 21 (21) ◽

pp. 7891

Author(s):

Chi-Wei Chen ◽

Lan-Ying Huang ◽

Chia-Feng Liao ◽

Kai-Po Chang ◽

Yen-Wei Chu

Keyword(s):

Feature Selection ◽

Protein Phosphorylation ◽

Phosphorylation Site ◽

Path Selection ◽

Ant Colony ◽

Ant Colony System ◽

Phosphorylation Sites ◽

Selection Approach ◽

New Feature ◽

Feature Selection Approach

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance.

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Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy

Briefings in Bioinformatics ◽

10.1093/bib/bby122 ◽

2018 ◽

Vol 21 (2) ◽

pp. 595-608 ◽

Cited By ~ 4

Author(s):

Man Cao ◽

Guodong Chen ◽

Jialin Yu ◽

Shaoping Shi

Keyword(s):

Protein Phosphorylation ◽

Computational Prediction ◽

Optimization Method ◽

Phosphorylation Sites ◽

Optimization Strategy ◽

Post Translational Modification ◽

Tyrosine Residues ◽

Identification Of Fungi ◽

Feature Optimization ◽

Species Specific

Abstract Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.

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