Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network - PubMed (original) (raw)
Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network
Jian Cui et al. PLoS One. 2011.
Abstract
Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome.
Conflict of interest statement
Competing Interests: The authors have declared that no competing interests exist.
Figures
Figure 1. Performance evaluation metrics of mitochondrial prediction methods.
(A) Sensitivity and false positive rate of Arabidopsis mitochondrial prediction methods. Using training data sets of 894 known Arabidopsis mitochondrial proteins (GSPmito) and 1,464 non-mitochondrial proteins(GSN∼mito), we estimated the sensitivity (percentage of GSPmito correctly predicted) and false positive rate of each prediction method. The accuracies of the thirteen individual data sets (s1∼s14) are shown at specific thresholds, while ArathMitoP set is drawn as a colorful ROC curve and the chosen threshold is noted with a red circle, at which we can obtain a good balance between the two performance metrics (i.e. FPR and sensitivity)…black circles indicate other s1–s14 predictive powers. (B). ROC curve of predictive powers for indirectly merged predictors, s1–s9. Blue circle indicates the threshold for indirectly merged s1–s9 predictors (named Group1); red circle indicates the power for ArathMitoP predictions, black circles indicate other s1–s14 predictive powers. (C). ROC curve of predictive powers for indirectly merged predictors, s10–s13; blue circle indicates the threshold for indirectly merged s10–s13 predictors (named Group2); red circle indicates the power for ArathMitoP predictions, black circles indicate s1–s14 predictive powers. (D). ROC curve of predictive powers for integrated three groups generated under the indirectly merge strategy. blue circle indicates the threshold for merged integration (named Merged Predictor); red circle indicates the power for ArathMitoP predictions, black circles indicate s1–s14 predictive powers.
Figure 2. The coverage of 14 individual predictors and the ArathMitoP set with GSPmito and GSN∼mito.
The number of overlapped proteins between si (i = 1,…,14) and training data sets is shown individually. Green bars indicate the intersection between si and GSPmito, while grey bars indicate the intersection between si and GSN∼mito.
Figure 3. The enrichments of major functional categories for proteins generated from the fourteen genome-wide predictors (s1–s14).
Fourteen major Gene Ontology categories were assigned to each genome-wide predictor and each functional category' enrichment for each predictor is shown in different colored bars. Grey bars indicate the proteins with unknown functions. Because of the relative quantity of predictors from s1–s9 generated by bioinformatics tools are large containing thousands of proteins, the percentage of unknown proteins is larger than that of other predictors. Predictors using homolog or orthologs methods have little unknown proteins. Meanwhile metabolism, energy, protein synthesis, transport functional categories are enriched in all predictors. However, DNA Synthesis and Processing are enriched only in predictors of s1∼s9, s13 and s14. Signaling transductions and cellular communications are not enriched in predictors like s11, s12, and s13, but enriched in other predictors.
Figure 4. Major function categories of proteins within ArathMitoP Set, CoreMitoP and GSPmito.
Twelve protein functional divisions are considered and used for function assignment to proteins in ArathMitoP, CoreMitoP and GSPmito with Gene Ontology annotation. Several major functions exerted by mitochondria include 1) cellular Communication/Signal Transduction, 2) Cellular Structural Organization, 3) Cellular Transport and Transport Mechanisms, 4) Defense stress and detoxification, 5) DNA Synthesis and Processing, 6) Energy, 7) Metabolism, 8) Miscellaneous Function, 9) Protein Fate, 10) Protein Synthesis, 11)RNA Processing, 12) Transcription and unclassified ones.
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