scholarly journals Analysis of Flagellar Phosphoproteins from Chlamydomonas reinhardtii

2009 ◽  
Vol 8 (7) ◽  
pp. 922-932 ◽  
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.

scholarly journals A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niraj Thapa ◽  
Meenal Chaudhari ◽  
Anthony A. Iannetta ◽  
Clarence White ◽  
Kaushik Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chlamydomonas Reinhardtii ◽  
Protein Phosphorylation ◽  
Short Term Memory ◽  
Phosphorylation Site ◽  
Specific Protein ◽  
Training Dataset ◽  
Phosphorylation Sites ◽  
Site Prediction ◽  
Model Combining

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.

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

PROTEOMICS ◽  
2009 ◽  
Vol 9 (20) ◽  
pp. 4642-4652 ◽  
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

scholarly journals Structure of the SAS-6 cartwheel hub from Leishmania major

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Mark van Breugel ◽  
Rainer Wilcken ◽  
Stephen H McLaughlin ◽  
Trevor J Rutherford ◽  
Christopher M Johnson
Keyword(s):  
Small Molecules ◽  
Self Assembly ◽  
Leishmania Major ◽  
Cell Organelles ◽  
Cylindrical Cell ◽  
X Ray ◽  
Cilia And Flagella ◽  
Proof Of Principle

Centrioles are cylindrical cell organelles with a ninefold symmetric peripheral microtubule array that is essential to template cilia and flagella. They are built around a central cartwheel assembly that is organized through homo-oligomerization of the centriolar protein SAS-6, but whether SAS-6 self-assembly can dictate cartwheel and thereby centriole symmetry is unclear. Here we show that Leishmania major SAS-6 crystallizes as a 9-fold symmetric cartwheel and provide the X-ray structure of this assembly at a resolution of 3.5 Å. We furthermore demonstrate that oligomerization of Leishmania SAS-6 can be inhibited by a small molecule in vitro and provide indications for its binding site. Our results firmly establish that SAS-6 can impose cartwheel symmetry on its own and indicate how this process might occur mechanistically in vivo. Importantly, our data also provide a proof-of-principle that inhibition of SAS-6 oligomerization by small molecules is feasible.

scholarly journals Molecular and Genetic Analysis of Two Closely Linked Genes That Encode, Respectively, a Protein Phosphatase 1/2A/2B Homolog and a Protein Kinase Homolog in the CyanobacteriumAnabaena sp. Strain PCC 7120

1998 ◽  
Vol 180 (10) ◽  
pp. 2616-2622 ◽  
Author(s):  
Cheng-Cai Zhang ◽  
Aline Friry ◽  
Ling Peng
Keyword(s):  
Protein Kinase ◽  
Protein Phosphorylation ◽  
Polyclonal Antibodies ◽  
Kinase Gene ◽  
Protein Kinase Gene ◽  
Reversible Protein Phosphorylation ◽  
Pcc 7120 ◽  
Correct Level ◽  
Nitrogen Conditions

ABSTRACT Reversible protein phosphorylation plays important roles in signal transduction. One gene, prpA, encoding a protein similar to eukaryotic types of phosphoprotein phosphatases PP1, PP2A, and PP2B, was cloned from the nitrogen-fixing cyanobacterium Anabaenasp. strain PCC 7120. Interestingly, a eukaryotic-type protein kinase gene, pknE, was found 301 bp downstream ofprpA. This unusual genetic arrangement provides the opportunity for study about how the balance between protein phosphorylation and dephosphorylation can regulate cellular activities. Both proteins were overproduced in Escherichia coli and used to raise polyclonal antibodies. Immunodetection and RNA/DNA hybridization experiments suggest that these two genes are unlikely to be coexpressed, despite their close genetic linkage. PrpA is expressed constitutively under different nitrogen conditions, while PknE expression varies according to the nature of the nitrogen source. Inactivation analysis in vivo suggests that PrpA and PknE function to ensure a correct level of phosphorylation of the targets in order to regulate similar biological processes such as heterocyst structure formation and nitrogen fixation.

Chlamy-EnPhosSite: A deep learning-based approach for Chlamydomonas reinhardtii-specific phosphorylation site prediction

2021 ◽  
Author(s):  
Niraj Thapa ◽  
Meenal Chaudhari ◽  
Anthony A. Iannetta ◽  
Clarence White ◽  
Kaushik Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chlamydomonas Reinhardtii ◽  
Protein Phosphorylation ◽  
Short Term Memory ◽  
Phosphorylation Site ◽  
Specific Protein ◽  
Training Dataset ◽  
Phosphorylation Sites ◽  
Site Prediction ◽  
Novel Approach

Abstract Protein phosphorylation is one of the most important post-translational modifications (PTMs) and involved in myriad cellular processes. Although many non-organism-specific computational phosphorylation site prediction tools and a few tools for organism-specific phosphorylation site prediction exist, none are currently available for Chlamydomonas reinhardtii. Herein, we present a novel deep learning (DL) based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. Our novel approach called Chlamy-EnPhosSite (based on ensemble approach combining convolutional neural networks (CNN) and long short-term memory LSTM) produces AUC and MCC of 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing. 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 90% 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 77% 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.

Identification of in vivo protein phosphorylation sites in human pathogen Schistosoma japonicum by a phosphoproteomic approach

2012 ◽  
Vol 75 (3) ◽  
pp. 868-877 ◽  
Author(s):  
Rong Luo ◽  
Chunjing Zhou ◽  
Jiaojiao Lin ◽  
Dehao Yang ◽  
Yaojun Shi ◽  
...  
Keyword(s):  
Protein Phosphorylation ◽  
Schistosoma Japonicum ◽  
Phosphorylation Sites ◽  
Human Pathogen

scholarly journals Proteomic analysis of a eukaryotic cilium

2005 ◽  
Vol 170 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Gregory J. Pazour ◽  
Nathan Agrin ◽  
John Leszyk ◽  
George B. Witman
Keyword(s):  
Mass Spectrometry ◽  
Signal Transduction ◽  
Life Cycles ◽  
Cystic Kidney Disease ◽  
Cystic Kidney ◽  
High Confidence ◽  
Cell Organelles ◽  
Complete Dataset ◽  
Flagellar Proteins ◽  
Cilia And Flagella

Cilia and flagella are widespread cell organelles that have been highly conserved throughout evolution and play important roles in motility, sensory perception, and the life cycles of eukaryotes ranging from protists to humans. Despite the ubiquity and importance of these organelles, their composition is not well known. Here we use mass spectrometry to identify proteins in purified flagella from the green alga Chlamydomonas reinhardtii. 360 proteins were identified with high confidence, and 292 more with moderate confidence. 97 out of 101 previously known flagellar proteins were found, indicating that this is a very complete dataset. The flagellar proteome is rich in motor and signal transduction components, and contains numerous proteins with homologues associated with diseases such as cystic kidney disease, male sterility, and hydrocephalus in humans and model vertebrates. The flagellum also contains many proteins that are conserved in humans but have not been previously characterized in any organism. The results indicate that flagella are far more complex than previously estimated.

scholarly journals PhosphoGRID: a database of experimentally verified in vivo protein phosphorylation sites from the budding yeast Saccharomyces cerevisiae

Database ◽  
2010 ◽  
Vol 2010 (0) ◽  
pp. bap026-bap026 ◽  
Author(s):  
C. Stark ◽  
T.-C. Su ◽  
A. Breitkreutz ◽  
P. Lourenco ◽  
M. Dahabieh ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Protein Phosphorylation ◽  
Budding Yeast ◽  
Phosphorylation Sites ◽  
Yeast Saccharomyces Cerevisiae
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