In silico methods for identifying organellar and suborganellar targeting peptides in Arabidopsis chloroplast proteins and for predicting the topology of membrane proteins

Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants

In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?

Cytosolic and Nuclear Co-localization of Betalain Biosynthetic Enzymes in Tobacco Suggests that Betalains Are Synthesized in the Cytoplasm and/or Nucleus of Betalainic Plant Cells

Betalains replace anthocyanins as color pigments in most families of Caryophyllales. Unlike anthocyanins, betalains are derived from tyrosine via three enzymatic steps: hydroxylation of L-tyrosine to L-3,4-dihydroxyphenylalanine (L-DOPA; step 1), and conversion of L-DOPA to betalamic acid (step 2), and to cyclo-DOPA (cDOPA; step 3). The principal enzymes responsible for these reactions have been elucidated at the molecular level, but their subcellular localizations have not been explored; hence, the intracellular compartments wherein betalains are biosynthesized remain unknown. Here, we report on the subcellular localization of these principal enzymes. Bioinformatic predictors and N- and C-terminal GFP tagging in transgenic tobacco, showed that Beta vulgaris CYP76AD1 which mediates both steps 1 and 3, DODA1 that catalyzes step 2, and CYP76AD6 which also mediates step 1, were similarly localized to the cytoplasm and nucleus (although the P450s were also weakly present in the endoplasmic reticulum). These two compartments were also the principal locations of Mirabilis jalapa cDOPA5GT. The cytoplasmic and nuclear co-localization of these key enzymes in tobacco suggests that betalains are biosynthesized in the cytoplasm and/or nucleus of betalain-containing plant cells. Elucidation of the subcellular compartmentation of betalain biosynthesis will facilitate the bioengineering of the betalain biosynthetic pathway in non-betalain-containing plants.

Subcellular Localization of a Plant Catalase-Phenol Oxidase, AcCATPO, from Amaranthus and Identification of a Non-canonical Peroxisome Targeting Signal

AcCATPO is a plant catalase-phenol oxidase recently identified from red amaranth. Its physiological function remains unexplored. As the starting step of functional analysis, here we report its subcellular localization and a non-canonical targeting signal. Commonly used bioinformatics programs predicted a peroxisomal localization for AcCATPO, but failed in identification of canonical peroxisomal targeting signals (PTS). The C-terminal GFP tagging led the fusion protein AcCATPO-GFP to the cytosol and the nucleus, but N-terminal tagging directed the GFP-AcCATPO to peroxisomes and nuclei, in transgenic tobacco. Deleting the tripeptide (PTM) at the extreme C-terminus almost ruled out the peroxisomal localization of GFP-AcCATPOΔ3, and removing the C-terminal decapeptide completely excluded peroxisomes as the residence of GFP-AcCATPOΔ10. Furthermore, this decapeptide as a targeting signal could import GFP-10aa to the peroxisome exclusively. Taken together, these results demonstrate that AcCATPO is localized to the peroxisome and the nucleus, and its peroxisomal localization is attributed to a non-canonical PTS1, the C-terminal decapeptide which contains an internal SRL motif and a conserved tripeptide P-S/T-I/M at the extreme of C-terminus. This work may further the study as to the physiological function of AcCATPO, especially clarify its involvement in betalain biosynthesis, and provide a clue to elucidate more non-canonic PTS.

SUBAcon: a consensus algorithm for unifying the subcellular localization data of the Arabidopsis proteome

MOTIVATION

Knowing the subcellular location of proteins is critical for understanding their function and developing accurate networks representing eukaryotic biological processes. Many computational tools have been developed to predict proteome-wide subcellular location, and abundant experimental data from green fluorescent protein (GFP) tagging or mass spectrometry (MS) are available in the model plant, Arabidopsis. None of these approaches is error-free, and thus, results are often contradictory.

RESULTS

To help unify these multiple data sources, we have developed the SUBcellular Arabidopsis consensus (SUBAcon) algorithm, a naive Bayes classifier that integrates 22 computational prediction algorithms, experimental GFP and MS localizations, protein-protein interaction and co-expression data to derive a consensus call and probability. SUBAcon classifies protein location in Arabidopsis more accurately than single predictors.

AVAILABILITY

SUBAcon is a useful tool for recovering proteome-wide subcellular locations of Arabidopsis proteins and is displayed in the SUBA3 database (http://suba.plantenergy.uwa.edu.au). The source code and input data is available through the SUBA3 server (http://suba.plantenergy.uwa.edu.au//SUBAcon.html) and the Arabidopsis SUbproteome REference (ASURE) training set can be accessed using the ASURE web portal (http://suba.plantenergy.uwa.edu.au/ASURE).

Fluorescent protein tagging as a tool to define the subcellular distribution of proteins in plants

Fluorescent protein (FP) tagging approaches are widely used to determine the subcellular location of plant proteins. Here we give a brief overview of FP approaches, highlight potential technical problems, and discuss what to consider when designing FP/protein fusion constructs and performing transformation assays. We analyze published FP tagging data sets along with data from proteomics studies collated in SUBA3, a subcellular location database for Arabidopsis proteins, and assess the reliability of these data sets by comparing them. We also outline the limitations of the FP tagging approach for defining protein location and investigate multiple localization claims by FP tagging. We conclude that the collation of localization datasets in databases like SUBA3 is helpful for revealing discrepancies in location attributions by different techniques and/or by different research groups.

SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis

The subcellular location database for Arabidopsis proteins (SUBA3, http://suba.plantenergy.uwa.edu.au) combines manual literature curation of large-scale subcellular proteomics, fluorescent protein visualization and protein-protein interaction (PPI) datasets with subcellular targeting calls from 22 prediction programs. More than 14 500 new experimental locations have been added since its first release in 2007. Overall, nearly 650 000 new calls of subcellular location for 35 388 non-redundant Arabidopsis proteins are included (almost six times the information in the previous SUBA version). A re-designed interface makes the SUBA3 site more intuitive and easier to use than earlier versions and provides powerful options to search for PPIs within the context of cell compartmentation. SUBA3 also includes detailed localization information for reference organelle datasets and incorporates green fluorescent protein (GFP) images for many proteins. To determine as objectively as possible where a particular protein is located, we have developed SUBAcon, a Bayesian approach that incorporates experimental localization and targeting prediction data to best estimate a protein's location in the cell. The probabilities of subcellular location for each protein are provided and displayed as a pictographic heat map of a plant cell in SUBA3.