Dataset Information

MCherry fusions enable the subcellular localization of periplasmic and cytoplasmic proteins in Xanthomonas sp.

ABSTRACT: Fluorescent markers are a powerful tool and have been widely applied in biology for different purposes. The genome sequence of Xanthomonas citri subsp. citri (X. citri) revealed that approximately 30% of the genes encoded hypothetical proteins, some of which could play an important role in the success of plant-pathogen interaction and disease triggering. Therefore, revealing their functions is an important strategy to understand the bacterium pathways and mechanisms involved in plant-host interaction. The elucidation of protein function is not a trivial task, but the identification of the subcellular localization of a protein is key to understanding its function. We have constructed an integrative vector, pMAJIIc, under the control of the arabinose promoter, which allows the inducible expression of red fluorescent protein (mCherry) fusions in X. citri, suitable for subcellular localization of target proteins. Fluorescence microscopy was used to track the localization of VrpA protein, which was visualized surrounding the bacterial outer membrane, and the GyrB protein, which showed a diffused cytoplasmic localization, sometimes with dots accumulated near the cellular poles. The integration of the vector into the amy locus of X. citri did not affect bacterial virulence. The vector could be stably maintained in X. citri, and the disruption of the ?-amylase gene provided an ease screening method for the selection of the transformant colonies. The results demonstrate that the mCherry-containing vector here described is a powerful tool for bacterial protein localization in cytoplasmic and periplasmic environments.

SUBMITTER: Pena MM

PROVIDER: S-EPMC7392301 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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mCherry fusions enable the subcellular localization of periplasmic and cytoplasmic proteins in Xanthomonas sp.

Pena Michelle Mendonça MM Teper Doron D Ferreira Henrique H Wang Nian N Sato Kenny Umino KU Ferro Maria Inês Tiraboschi MIT Ferro Jesus Aparecido JA

PloS one 20200730 7

Fluorescent markers are a powerful tool and have been widely applied in biology for different purposes. The genome sequence of Xanthomonas citri subsp. citri (X. citri) revealed that approximately 30% of the genes encoded hypothetical proteins, some of which could play an important role in the success of plant-pathogen interaction and disease triggering. Therefore, revealing their functions is an important strategy to understand the bacterium pathways and mechanisms involved in plant-host intera ...[more]

PMID: 32730344

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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.

Project description:BackgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.ResultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.ConclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.

Dataset Information

MCherry fusions enable the subcellular localization of periplasmic and cytoplasmic proteins in Xanthomonas sp.

Publications

mCherry fusions enable the subcellular localization of periplasmic and cytoplasmic proteins in Xanthomonas sp.

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