Refining Protein Subcellular Localization

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality

Alteration of host cell ubiquitination by the intracellular bacterial pathogen Coxiella burnetii

The intracellular bacterial agent of Q fever, Coxiella burnetii, replicates within a phagolysosomelike parasitophorous vacuole (PV) in human macrophages and delivers effector proteins to the host cytosol via a Dot/Icm type IV secretion system (T4SS). The T4SS effectors are critical for PV formation and prevention of host cell death that allows sufficient time for bacterial replication. Recruitment of ubiquitin-related components to the C. burnetii PV is also predicted to be involved in PV formation and bacterial replication and is likely controlled by effector proteins. In this study, we assessed the role of the Dot/Icm T4SS in regulating ubiquitination by comparing subcellular localization of ubiquitinated proteins between cells infected with C. burnetii and a mutant that lacks a functional T4SS. Fluorescence microscopy showed ubiquitinated proteins surrounding wild-type C. burnetii PV but not phagosomes harboring T4SS-defective organisms. Immunoblot analysis showed altered ubiquitinated protein profiles throughout infection, suggesting C. burnetii impacts post-translational modification of host cell and/or bacterial proteins. Future studies will determine how T4SS-mediated recruitment of ubiquitinated proteins impacts C. burnetii-host cell interactions and eventual development of diseas

Bacterial riboproteogenomics : the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome re-annotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms

The Yeast Resource Center Public Image Repository: A large database of fluorescence microscopy images

<p>Abstract</p> <p>Background</p> <p>There is increasing interest in the development of computational methods to analyze fluorescent microscopy images and enable automated large-scale analysis of the subcellular localization of proteins. Determining the subcellular localization is an integral part of identifying a protein's function, and the application of bioinformatics to this problem provides a valuable tool for the annotation of proteomes. Training and validating algorithms used in image analysis research typically rely on large sets of image data, and would benefit from a large, well-annotated and highly-available database of images and associated metadata.</p> <p>Description</p> <p>The Yeast Resource Center Public Image Repository (YRC PIR) is a large database of images depicting the subcellular localization and colocalization of proteins. Designed especially for computational biologists who need large numbers of images, the YRC PIR contains 532,182 TIFF images from nearly 85,000 separate experiments and their associated experimental data. All images and associated data are searchable, and the results browsable, through an intuitive web interface. Search results, experiments, individual images or the entire dataset may be downloaded as standards-compliant OME-TIFF data.</p> <p>Conclusions</p> <p>The YRC PIR is a powerful resource for researchers to find, view, and download many images and associated metadata depicting the subcellular localization and colocalization of proteins, or classes of proteins, in a standards-compliant format. The YRC PIR is freely available at <url>http://images.yeastrc.org/</url>.</p

Semi-supervised protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.</p> <p>Results</p> <p>In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.</p> <p>Conclusion</p> <p>Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.</p

Large-scale prediction and analysis of protein sub-mitochondrial localization with DeepMito

Background: The prediction of protein subcellular localization is a key step of the big effort towards protein functional annotation. Many computational methods exist to identify high-level protein subcellular compartments such as nucleus, cytoplasm or organelles. However, many organelles, like mitochondria, have their own internal compartmentalization. Knowing the precise location of a protein inside mitochondria is crucial for its accurate functional characterization. We recently developed DeepMito, a new method based on a 1-Dimensional Convolutional Neural Network (1D-CNN) architecture outperforming other similar approaches available in literature. Results: Here, we explore the adoption of DeepMito for the large-scale annotation of four sub-mitochondrial localizations on mitochondrial proteomes of five different species, including human, mouse, fly, yeast and Arabidopsis thaliana. A significant fraction of the proteins from these organisms lacked experimental information about sub-mitochondrial localization. We adopted DeepMito to fill the gap, providing complete characterization of protein localization at sub-mitochondrial level for each protein of the five proteomes. Moreover, we identified novel mitochondrial proteins fishing on the set of proteins lacking any subcellular localization annotation using available state-of-the-art subcellular localization predictors. We finally performed additional functional characterization of proteins predicted by DeepMito as localized into the four different sub-mitochondrial compartments using both available experimental and predicted GO terms. All data generated in this study were collected into a database called DeepMitoDB (available at http://busca.biocomp.unibo.it/deepmitodb), providing complete functional characterization of 4307 mitochondrial proteins from the five species. Conclusions: DeepMitoDB offers a comprehensive view of mitochondrial proteins, including experimental and predicted fine-grain sub-cellular localization and annotated and predicted functional annotations. The database complements other similar resources providing characterization of new proteins. Furthermore, it is also unique in including localization information at the sub-mitochondrial level. For this reason, we believe that DeepMitoDB can be a valuable resource for mitochondrial research

Isoform-specific dynamic translocation of PKC by α1-adrenoceptor stimulation in live cells.

Protein kinase C (PKC) plays key roles in the regulation of signal transduction and cellular function in various cell types. At least ten PKC isoforms have been identified and intracellular localization and trafficking of these individual isoforms are important for regulation of enzyme activity and substrate specificity. PKC can be activated downstream of Gq-protein coupled receptor (GqPCR) signaling and translocate to various cellular compartments including plasma membrane (PM). Recent reports suggested that different types of GqPCRs would activate different PKC isoforms (classic, novel and atypical PKCs) with different trafficking patterns. However, the knowledge of isoform-specific activation of PKC by each GqPCR is limited. α1-Adrenoceptor (α1-AR) is one of the GqPCRs highly expressed in the cardiovascular system. In this study, we examined the isoform-specific dynamic translocation of PKC in living HEK293T cells by α1-AR stimulation (α1-ARS). Rat PKCα, βI, βII, δ, ε and ζ fused with GFP at C-term were co-transfected with human α1A-AR into HEK293T cells. The isoform-specific dynamic translocation of PKC in living HEK293T cells by α1-ARS using phenylephrine was measured by confocal microscopy. Before stimulation, GFP-PKCs were localized at cytosolic region. α1-ARS strongly and rapidly translocated a classical PKC (cPKC), PKCα, (\u3c30 \u3es) to PM, with PKCα returning diffusively into the cytosol within 5 min. α1-ARS rapidly translocated other cPKCs, PKCβI and PKCβII, to the PM (\u3c30 \u3es), with sustained membrane localization. One novel PKC (nPKC), PKCε, but not another nPKC, PKCδ, was translocated by α1-AR stimulation to the PM (\u3c30 \u3es) and its membrane localization was also sustained. Finally, α1-AR stimulation did not cause a diacylglycerol-insensitive atypical PKC, PKCζ translocation. Our data suggest that PKCα, β and ε activation may underlie physiological and pathophysiological responses of α1-AR signaling for the phosphorylation of membrane-associated substrates including ion-channel and transporter proteins in the cardiovascular system

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p

Determining the Localization of NCOA7-AS Through GFP Tagging and Fluorescence Microscopy

In Multiple Sclerosis patients, previous experiments have determined the structure, potential function, and regulation of the oxidative damage resistance protein nuclear receptor coactivator-7 alternate start (NCOA7-AS) through induction with Interferon-Beta (IFN-Beta). However, the subcellular localization of NCOA7-AS remains unknown. In this project, the localization will be determined by fusing GFP to NCOA7-AS, expressing the fusion protein in HT1080 and HeLa cells, and using fluorescence microscopy. Cells will also be treated with IFN-Beta to see if this alters NCOA7-AS localization. The GFP microscopy data indicate that NCOA7-AS localization is diffuse in cells with no clear subcellular localization, and that addition of IFN-Beta does not alter the localization