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Discovering and validating unknown phospho-sites from p38 and HuR protein kinases in vitro by Phosphoproteomic and Bioinformatic tools.

ABSTRACT: BACKGROUND:The mitogen activated protein kinase (MAPK) pathways are known to be deregulated in many human malignancies. Phosphopeptide identification of protein-kinases and site determination are major challenges in biomedical mass spectrometry (MS). P38 and HuR protein kinases have been reported extensively in the general principles of signalling pathways modulated by phosphorylation, mainly by molecular biology and western blotting techniques. Thus, although it has been demonstrated they are phosphorylated in different stress/stimuli conditions, the phosphopeptides and specific amino acids in which the phosphate groups are located in those protein kinases have not been shown completely. METHODS:We have combined different resins: (a) IMAC (Immobilized Metal Affinity Capture), (b) TiO2 (Titanium dioxide) and (c) SIMAC (Sequential Elution from IMAC) to isolate phosphopeptides from p38 and HuR protein kinases in vitro.Different phosphopeptide MS strategies were carried out by the LTQ ion Trap mass spectrometer (Thermo): (a) Multistage activation (MSA) and (b) Neutral loss MS3 (DDNLMS3).In addition, Molecular Dynamics (MD) bioinformatic simulation has been applied in order to simulate, over a period of time, the effects of the presence of the extra phosphate group (and the associated negative charge) in the overall structure and behaviour of the protein HuR.This study is supported by the Declaration of Helsinki and subsequent ethical guidelines. RESULTS:The combination of these techniques allowed for:(1) The identification of 6 unknown phosphopeptides of these protein kinases. (2) Amino acid site assignments of the phosphate groups from each identified phosphopeptide, including manual validation by inspection of all the spectra. (3) The analyses of the phosphopeptides discovered were carried out in four triplicate experiments to avoid false positives getting high reproducibility in all the isolated phosphopeptides recovered from both protein kinases. (4) Computer simulation using MD techniques allowed us to get functional models of both structure and interactions of the previously mentioned phosphorylated kinases and the differences between their phosphorylated and un-phosphorylated forms. CONCLUSION:Many research studies are necessary to unfold the whole signalling network (human proteome), which is so important to advance in clinical research, especially in the cases of malignant diseases.

SUBMITTER: Lopez E

PROVIDER: S-EPMC3164609 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Discovering and validating unknown phospho-sites from p38 and HuR protein kinases in vitro by Phosphoproteomic and Bioinformatic tools.

López Elena E López Isabel I Sequí Julia J Ferreira Antonio A

Journal of clinical bioinformatics 20110706 1

<h4>Background</h4>The mitogen activated protein kinase (MAPK) pathways are known to be deregulated in many human malignancies. Phosphopeptide identification of protein-kinases and site determination are major challenges in biomedical mass spectrometry (MS). P38 and HuR protein kinases have been reported extensively in the general principles of signalling pathways modulated by phosphorylation, mainly by molecular biology and western blotting techniques. Thus, although it has been demonstrated th ...[more]

PMID: 21884634

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Project description:BackgroundThe computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins.ResultsA final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out.ConclusionBoth subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model.

Dataset Information

Discovering and validating unknown phospho-sites from p38 and HuR protein kinases in vitro by Phosphoproteomic and Bioinformatic tools.

Publications

Discovering and validating unknown phospho-sites from p38 and HuR protein kinases in vitro by Phosphoproteomic and Bioinformatic tools.

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