Detection of protein stoichiometric phosphorylation using Phos-tag SDS-PAGE

Protein phosphorylation plays an important role in many cellular signalings which are relating to many diseases. Therefore, a variety of biochemical techniques has been developed to study protein phosphorylation in cells. Protein phosphorylation has traditionally been detected by radioisotope phosphate labeling of proteins with radioactive ATP. Phosphorylation site-specific antibodies are now available for the analysis of phosphorylation status at target sites. However, these antibodies cannot be used to detect unidentified phosphorylation sites. Recently, the Phos-tag technology has been developed to overcome the disadvantages and limitations of these methods. Phos-tag and its derivatives conjugated to biotin, acrylamide, or agarose, and can capture phosphate monoester dianions bound to serine, threonine, and tyrosine residues, in an amino acid sequence-independent manner. The grouping of the Phos-tag will alter the mobility of protein on the gel depending on the amount of serine, threonine or tyrosine which are phosphorylated. Here, we describe the method to detect the phosphorylation of Pop2 protein, one of the exonucleases in the Ccr4-Not complex regulating the shortening of poly(A) tail of mRNAs using phosphate affinity Phos-tag SDS-PAGE. We observed clear electrophoretic 04 shift bands of Pop2-3XFlag under unstressed conditions. This is the first study which observes Pop2 phosphorylation in normal culture conditions. This study showed the convenience and advantages of Phos-tag SDS-PAGE for research on molecular mechanisms regulating the function of protein.

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

Scientific Reports ◽

10.1038/s41598-021-91840-w ◽

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.

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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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Synthetic-evolution reveals that phosphoregulation of the mitotic kinesin-5 Cin8 is constrained

10.1101/312637 ◽

2018 ◽

Cited By ~ 2

Author(s):

Alina Goldstein ◽

Darya Goldman ◽

Ervin Valk ◽

Mart Loog ◽

Liam J. Holt ◽

...

Keyword(s):

Amino Acid ◽

Phosphorylation Site ◽

Motor Domain ◽

Single Amino Acid ◽

Amino Acid Substitutions ◽

Phosphorylation Sites ◽

Wild Type ◽

Site Specific ◽

Precise Positioning ◽

Optimal Regulation

AbstractCdk1 has been found to phosphorylate the majority of its substrates in disordered regions. These phosphorylation sites do not appear to require precise positioning for their function. The mitotic kinesin-5 Cin8 was shown to be phosphoregulated at three Cdk1 sites in disordered loops within its catalytic motor domain. Here, we examined the flexibility of Cin8 phosphoregulation by analyzing the phenotypes of synthetic Cdk1-sites that were systematically generated by single amino-acid substitutions, starting from a phosphodeficient variant of Cin8. Out of 29 synthetic Cdk1 sites that we created, eight were non-functional; 19 were neutral, similar to the phosphodeficient variant; and two gave rise to phosphorylation-dependent spindle phenotypes. Of these two, one site resulted in novel phosphoregulation, and only one site, immediately adjacent to a native Cdk1 site, produced phosphoregulation similar to wild-type. This study shows that, while the gain of a single phosphorylation site can confer regulation and modulate the dynamics of the spindle, to achieve optimal regulation of a mitotic kinesin-5 motor protein, phosphoregulation has to be site-specific and precise.

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

10.21203/rs.3.rs-286990/v1 ◽

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.

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DeepPhos: prediction of protein phosphorylation sites with deep learning

Bioinformatics ◽

10.1093/bioinformatics/bty1051 ◽

2019 ◽

Vol 35 (16) ◽

pp. 2766-2773 ◽

Cited By ~ 35

Author(s):

Fenglin Luo ◽

Minghui Wang ◽

Yu Liu ◽

Xing-Ming Zhao ◽

Ao Li

Keyword(s):

Deep Learning ◽

Protein Phosphorylation ◽

Phosphorylation Site ◽

Supplementary Information ◽

Phosphorylation Sites ◽

Post Translational Modification ◽

Neuron Network ◽

Site Prediction ◽

Specific Prediction ◽

Group Family

Abstract Motivation Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but most of them are based on feature selection and discriminative classification. Thus, it is useful to develop a novel and highly accurate predictor that can unveil intricate patterns automatically for protein phosphorylation sites. Results In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers. DeepPhos can also be used for kinase-specific prediction varying from group, family, subfamily and individual kinase level. The experimental results demonstrated that DeepPhos outperforms competitive predictors in general and kinase-specific phosphorylation site prediction. Availability and implementation The source code of DeepPhos is publicly deposited at https://github.com/USTCHIlab/DeepPhos. Supplementary information Supplementary data are available at Bioinformatics online.

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EPSD: a well-annotated data resource of protein phosphorylation sites in eukaryotes

Briefings in Bioinformatics ◽

10.1093/bib/bbz169 ◽

2020 ◽

Cited By ~ 2

Author(s):

Shaofeng Lin ◽

Chenwei Wang ◽

Jiaqi Zhou ◽

Ying Shi ◽

Chen Ruan ◽

...

Keyword(s):

Protein Phosphorylation ◽

Phosphorylation Site ◽

Model Organisms ◽

Functional Domain ◽

Phosphorylation Sites ◽

Post Translational Modification ◽

Structural Annotation ◽

Data Resource ◽

Protein Protein Interaction ◽

Almost All

Abstract As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization. We anticipate that the EPSD can serve as a useful resource for further analysis of eukaryotic phosphorylation. With a data volume of 14.1 GB, EPSD is free for all users at http://epsd.biocuckoo.cn/.

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Data analysis and prediction of protein posttranslational modification

10.32469/10355/63994 ◽

2014 ◽

Author(s):

◽

Qiuming Yao

Keyword(s):

Mass Spectrometry ◽

Protein Phosphorylation ◽

Posttranslational Modification ◽

Experimental Approach ◽

High Resolution Mass Spectrometry ◽

Phosphorylation Site ◽

Phosphorylation Sites ◽

Post Translational Modification ◽

Substrate Network ◽

Resolution Mass

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Protein posttranslational modification (PTM) occurs broadly after or during protein biosynthesis, to assist folding or activate function during the protein lifetime. Among all types of PTMs, protein phosphorylation is widely recognized as the most pervasive, enzyme-catalyzed post-translational modification in eukaryotes. In particular, plants have higher magnitude of this signaling mechanism in terms of the protein kinase frequency within the genome compared to other eukaryotes. Phosphorylation site mapping using high-resolution mass spectrometry has grown exponentially. In Arabidopsis alone there are thousands of experimentally-determined phosphorylation sites. Likewise, other types of post translational modification data are rapidly increasing too. Acetylation proteome is another big data set in PTM kingdom. To provide an easy access of these modification events in a user-intuitive format we have developed P3DB, The Plant Protein Phosphorylation Database (p3db.org). This database is a repository for plant protein phosphorylation site data. These data can be queried for a protein-of-interest using an integrated BLAST function to search for similar sequences with known phosphorylation sites among the multiple plants currently investigated. Thus, this resource can help identify functionally-conserved phosphorylation sites in plants using a multi-system approach. Centralized by these phosphorylation data, multiple related data and annotations are provided, including protein-protein interaction (PPI), gene ontology, protein tertiary structures, orthologous sequences, kinase/phosphatase classification and Kinase Client Assay (KiC Assay) data. P3DB thus is not only a repository, but also a context provider for studying phosphorylation events. In addition, P3DB incorporates multiple network viewers for the above features, such as PPI network, kinase-substrate network, phosphatase-substrate network, and domain co-occurrence network to help study phosphorylation from a systems point of view. Furthermore, P3DB reflects a community-based design through which users can share data sets and automate data depository processes for publication purposes. Since P3DB is a comprehensive, systematic, and interactive platform for phosphoproteomics research, many data analyses can be done based on it. For example, the disorder analysis and the sequence conservation can be done based on the P3DB datasets. Many researchers downloaded and did some meaningful analysis based on P3DB infrastructure. Although with the development of the high-resolution mass spectrometry protein phosphorylation sites can be reliably identified, the experimental approach is time-consuming and resource-dependent. Furthermore, it is unlikely that an experimental approach could catalog an entire phosphoproteome. Computational prediction of phosphorylation sites provides an efficient and flexible way to reveal potential phosphorylation sites, facilitate experimental phosphorylation site identification and provide hypotheses in experimental design. Musite is a powerful tool that we developed to predict phosphorylation sites based solely on protein sequence. Musite integrates data preprocessing, feature extraction, machine-learning method, and prediction models into one comprehensive tool. Musite (http://musite.net) can be extended to all types of post translational modification study, as long as the dataset contains sufficient modification sites. To further improve the performance of Musite, a generalized motif tree applying fuzzy logic is introduced to compensate the machine learning based prediction. On one hand, using a tree based approach and fuzzy variables help to interpret the final rules, in order to help biologists to obtain the significant patterns. On the other hand, its extracted rule sets essentially generalize the motifs and reveal more information. It can be paired with traditional classification method and provide better interpretation, pre-filtering and analyzing power. Comparing to traditional motif extraction, the fuzzy motif decision tree is able to borrow more information from the observations and thus it may extract more novel motifs or more comprehensive patterns. It can be applied on kinase specific phosphorylated peptides to achieve more insights of the phosphorylation events. A comprehensive database (P3DB), a well-developed prediction tool (Musite), and a generalized motif constructor (Fuzzy Motif Tree) combined enable researchers to investigate the phosphorylation and other posttranslational modification events more thoroughly and thus to reveal more underlying biological significance by applying these computational resources.

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Proteolytic cleavage and PKA phosphorylation of α1C subunit are not required for adrenergic regulation of CaV1.2 in the heart

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1706054114 ◽

2017 ◽

Vol 114 (34) ◽

pp. 9194-9199 ◽

Cited By ~ 21

Author(s):

Alexander Katchman ◽

Lin Yang ◽

Sergey I. Zakharov ◽

Jared Kushner ◽

Jeffrey Abrams ◽

...

Keyword(s):

Transgenic Mice ◽

Molecular Mechanisms ◽

Calcium Influx ◽

Phosphorylation Site ◽

Proteolytic Cleavage ◽

Phosphorylation Sites ◽

Adrenergic Stimulation ◽

Adrenergic Regulation ◽

Voltage Dependent ◽

Stimulation Of

Calcium influx through the voltage-dependent L-type calcium channel (CaV1.2) rapidly increases in the heart during “fight or flight” through activation of the β-adrenergic and protein kinase A (PKA) signaling pathway. The precise molecular mechanisms of β-adrenergic activation of cardiac CaV1.2, however, are incompletely known, but are presumed to require phosphorylation of residues in α1C and C-terminal proteolytic cleavage of the α1C subunit. We generated transgenic mice expressing an α1C with alanine substitutions of all conserved serine or threonine, which is predicted to be a potential PKA phosphorylation site by at least one prediction tool, while sparing the residues previously shown to be phosphorylated but shown individually not to be required for β-adrenergic regulation of CaV1.2 current (17-mutant). A second line included these 17 putative sites plus the five previously identified phosphoregulatory sites (22-mutant), thus allowing us to query whether regulation requires their contribution in combination. We determined that acute β-adrenergic regulation does not require any combination of potential PKA phosphorylation sites conserved in human, guinea pig, rabbit, rat, and mouse α1C subunits. We separately generated transgenic mice with inducible expression of proteolytic-resistant α1C. Prevention of C-terminal cleavage did not alter β-adrenergic stimulation of CaV1.2 in the heart. These studies definitively rule out a role for all conserved consensus PKA phosphorylation sites in α1C in β-adrenergic stimulation of CaV1.2, and show that phosphoregulatory sites on α1C are not redundant and do not each fractionally contribute to the net stimulatory effect of β-adrenergic stimulation. Further, proteolytic cleavage of α1C is not required for β-adrenergic stimulation of CaV1.2.

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Contraction regulates site-specific phosphorylation of TBC1D1 in skeletal muscle

Biochemical Journal ◽

10.1042/bj20101100 ◽

2010 ◽

Vol 431 (2) ◽

pp. 311-320 ◽

Cited By ~ 62

Author(s):

Kanokwan Vichaiwong ◽

Suneet Purohit ◽

Ding An ◽

Taro Toyoda ◽

Niels Jessen ◽

...

Keyword(s):

Skeletal Muscle ◽

Glucose Uptake ◽

Phosphorylation Site ◽

Phosphorylation Sites ◽

Site Specific ◽

Akt Phosphorylation ◽

And Function ◽

Amp Activated Protein Kinase ◽

Kinase Phosphorylation

TBC1D1 (tre-2/USP6, BUB2, cdc16 domain family member 1) is a Rab-GAP (GTPase-activating protein) that is highly expressed in skeletal muscle, but little is known about TBC1D1 regulation and function. We studied TBC1D1 phosphorylation on three predicted AMPK (AMP-activated protein kinase) phosphorylation sites (Ser231, Ser660 and Ser700) and one predicted Akt phosphorylation site (Thr590) in control mice, AMPKα2 inactive transgenic mice (AMPKα2i TG) and Akt2-knockout mice (Akt2 KO). Muscle contraction significantly increased TBC1D1 phosphorylation on Ser231 and Ser660, tended to increase Ser700 phosphorylation, but had no effect on Thr590. AICAR (5-aminoimidazole-4-carboxyamide ribonucleoside) also increased phosphorylation on Ser231, Ser660 and Ser700, but not Thr590, whereas insulin only increased Thr590 phosphorylation. Basal and contraction-stimulated TBC1D1 Ser231, Ser660 and Ser700 phosphorylation were greatly reduced in AMPKα2i TG mice, although contraction still elicited a small increase in phosphorylation. Akt2 KO mice had blunted insulin-stimulated TBC1D1 Thr590 phosphorylation. Contraction-stimulated TBC1D1 Ser231 and Ser660 phosphorylation were normal in high-fat-fed mice. Glucose uptake in vivo was significantly decreased in tibialis anterior muscles overexpressing TBC1D1 mutated on four predicted AMPK phosphorylation sites. In conclusion, contraction causes site-specific phosphorylation of TBC1D1 in skeletal muscle, and TBC1D1 phosphorylation on AMPK sites regulates contraction-stimulated glucose uptake. AMPK and Akt regulate TBC1D1 phosphorylation, but there must be additional upstream kinases that mediate TBC1D1 phosphorylation in skeletal muscle.

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