Protein-protein interaction as a predictor of subcellular location

Background  

Many biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.     

SCL-Epred: a generalised de novo eukaryotic protein subcellular localisation predictor

Knowledge of the subcellular location of a protein provides valuable information about its function, possible interaction with other proteins and drug targetability, among other things. The experimental determination of a protein’s location in the cell is expensive, time consuming and open to human error. Fast and accurate predictors of subcellular location have an important role to play if the abundance of sequence data which is now available is to be fully exploited. In the post-genomic era, genomes in many diverse organisms are available. Many of these organisms are important in human and veterinary disease and fall outside of the well-studied plant, animal and fungi groups. We have developed a general eukaryotic subcellular localisation predictor (SCL-Epred) which predicts the location of eukaryotic proteins into three classes which are important, in particular, for determining the drug targetability of a protein—secreted proteins, membrane proteins and proteins that are neither secreted nor membrane. The algorithm powering SCL-Epred is a N-to-1 neural network and is trained on very large non-redundant sets of protein sequences. SCL-Epred performs well on training data achieving a Q of 86 % and a generalised correlation of 0.75 when tested in tenfold cross-validation on a set of 15,202 redundancy reduced protein sequences. The three class accuracy of SCL-Epred and LocTree2, and in particular a consensus predictor comprising both methods, surpasses that of other widely used predictors when benchmarked using a large redundancy reduced independent test set of 562 proteins. SCL-Epred is publicly available at http://distillf.ucd.ie/distill/.     

Aspergillus niger G proteins: subcellular localization

Aspergillus niger postmitochondrial fraction, which contains high GTPase activity and high GTP binding capacity, has been subjected to subcellular fractionation on a sucrose gradient. A cytosolic and four membranous populations have been separated according to their relative density. The main difficulty has been the characterization of the plasma membrane of the fungus. This fraction, which does not contain any typical enzyme, has been identified after iodination of the outer proteins of protoplasts from A. niger. The immunological detection has shown the occurrence of cytosolic G proteins and membranous small G proteins located not only in the plasma membrane but also in the membranes of the endoplasmic reticulum.     

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.     

Self-calibrating viscosity probes: design and subcellular localization

We describe the design, synthesis and fluorescence profiles of new self-calibrating viscosity dyes in which a coumarin (reference fluorophore) has been covalently linked with a molecular rotor (viscosity sensor). Characterization of their fluorescence properties was made with separate excitation of the units and through resonance energy transfer from the reference to the sensor dye. We have modified the linker and the substitution of the rotor in order to change the hydrophilicity of these probes thereby altering their subcellular localization. For instance, hydrophilic dye 12 shows a homogeneous distribution inside the cell and represents a suitable probe for viscosity measurements in the cytoplasm.     

Sequence conserved for subcellular localization

The more proteins diverged in sequence, the more difficult it becomes for bioinformatics to infer similarities of protein function and structure from sequence. The precise thresholds used in automated genome annotations depend on the particular aspect of protein function transferred by homology. Here, we presented the first large-scale analysis of the relation between sequence similarity and identity in subcellular localization. Three results stood out: (1) The subcellular compartment is generally more conserved than what might have been expected given that short sequence motifs like nuclear localization signals can alter the native compartment; (2) the sequence conservation of localization is similar between different compartments; and (3) it is similar to the conservation of structure and enzymatic activity. In particular, we found the transition between the regions of conserved and nonconserved localization to be very sharp, although the thresholds for conservation were less well defined than for structure and enzymatic activity. We found that a simple measure for sequence similarity accounting for pairwise sequence identity and alignment length, the HSSP distance, distinguished accurately between protein pairs of identical and different localizations. In fact, BLAST expectation values outperformed the HSSP distance only for alignments in the subtwilight zone. We succeeded in slightly improving the accuracy of inferring localization through homology by fine tuning the thresholds. Finally, we applied our results to the entire SWISS-PROT database and five entirely sequenced eukaryotes.     

Prediction of protein subcellular localization

Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.     

Variable subcellular localization of glycosphingolipids

Although most glycosphingolipids (GSLs) are thought to be locatedin the outer leaflet of the plasma membrane, recent evidenceindicates that GSLs are also associated with intracellular organelles.We now report that the subcellular localization of GSLs variesdepending on the GSL structure and cell type. GSL localizationwas determined by indirect immunofluorescence microscopy offixed permeabilized cells. A single GSL exhibited variable subcellularlocalization in different cells. For example, antibody to GalCeris localized primarily to the plasma membrane of HaCaT II-3keratinocytes, but to intracellular organelies in other epithelialcells. GalCer is localized to small vesicles and tubulovesicularstructures in MDCK cells, and to the surface of phase-denselipid droplets in HepG2 hepatoma cells. Furthermore, withina single cell type, individual GSLs were found to exhibit differentpatterns of subcellular localization. In HepG2 cells, LacCerwas associated with small vesicles, which differed from thephase-dense vesicles stained by anti-GalCer, and Gb4Cer wasassociated with the intermediate filaments of the cytoskeleton.Both anti-GalCer and monoclonal antibody A2B5, which binds polysialogangliosides,localized to mitochondria. The distinct subcellular localizationpatterns of GSLs raise interesting questions about their functionsin different organelles. Together with published data on theenrichment of GSLs in specific organelles and in apical plasmamembrane, these findings indicate the existence of specificsorting mechanisms that regulate the intracellular transportand localization of GSLs. cytoskeleton glycosphingolipid intracellular organelles mitochondria subcellular localization     

Predicting subcellular localization of proteins in a hybridization space

MOTIVATION: The localization of a protein in a cell is closely correlated with its biological function. With the number of sequences entering into databanks rapidly increasing, the importance of developing a powerful high-throughput tool to determine protein subcellular location has become self-evident. In view of this, the Nearest Neighbour Algorithm was developed for predicting the protein subcellular location using the strategy of hybridizing the information derived from the recent development in gene ontology with that from the functional domain composition as well as the pseudo amino acid composition. RESULTS: As a showcase, the same plant and non-plant protein datasets as investigated by the previous investigators were used for demonstration. The overall success rate of the jackknife test for the plant protein dataset was 86%, and that for the non-plant protein dataset 91.2%. These are the highest success rates achieved so far for the two datasets by following a rigorous cross-validation test procedure, suggesting that such a hybrid approach (particularly by incorporating the knowledge of gene ontology) may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology. AVAILABILITY: The software would be made available on sending a request to the authors.     

Movement and subcellular localization of a tobamovirus in Arabidopsis

Tobamoviruses represent a well-characterized system used to examine viral infection, whereas Arabidopsis is a choice plant for most genetic experiments. It would be useful to combine both approaches into one experimental system for virus–plant interaction. Most tobamoviruses, however, are not pathogenic in Arabidopsis . Here, we describe infection of Arabidopsis by a recently discovered crucifer-infecting turnip vein clearing tobamovirus (TVCV). Using this system, we determined patterns and kinetics of viral local and systemic movement within Arabidopsis plants. Localization studies showed that the virus infects both vegetative and reproductive plant tissues. However, there may be a transport barrier between the seed coat and the embryo which virions cannot cross, preventing seed transmission of TVCV. The ability to move both locally and systemically in Arabidopsis , causing mild and fast-developing symptoms but allowing survival and fertility of the infected plants, distinguish TVCV infection of Arabidopsis as a model system to study virus–plant interaction.     

Prediction of protein subcellular locations by GO-FunD-PseAA predictor

The localization of a protein in a cell is closely correlated with its biological function. With the explosion of protein sequences entering into DataBanks, it is highly desired to develop an automated method that can fast identify their subcellular location. This will expedite the annotation process, providing timely useful information for both basic research and industrial application. In view of this, a powerful predictor has been developed by hybridizing the gene ontology approach [Nat. Genet. 25 (2000) 25], functional domain composition approach [J. Biol. Chem. 277 (2002) 45765], and the pseudo-amino acid composition approach [Proteins Struct. Funct. Genet. 43 (2001) 246; Erratum: ibid. 44 (2001) 60]. As a showcase, the recently constructed dataset [Bioinformatics 19 (2003) 1656] was used for demonstration. The dataset contains 7589 proteins classified into 12 subcellular locations: chloroplast, cytoplasmic, cytoskeleton, endoplasmic reticulum, extracellular, Golgi apparatus, lysosomal, mitochondrial, nuclear, peroxisomal, plasma membrane, and vacuolar. The overall success rate of prediction obtained by the jackknife cross-validation was 92%. This is so far the highest success rate performed on this dataset by following an objective and rigorous cross-validation procedure.     

TESTLoc: protein subcellular localization prediction from EST data

Background  

The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes.     

PSLpred: prediction of subcellular localization of bacterial proteins

SUMMARY: We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict approximately 74% of sequences with an average prediction accuracy of 98% at RI = 5. AVAILABILITY: PSLpred is available at http://www.imtech.res.in/raghava/pslpred/     

Predicting subcellular localization with AdaBoost Learner

Protein subcellular localization, which tells where a protein resides in a cell, is an important characteristic of a protein, and relates closely to the function of proteins. The prediction of their subcellular localization plays an important role in the prediction of protein function, genome annotation and drug design. Therefore, it is an important and challenging role to predict subcellular localization using bio-informatics approach. In this paper, a robust predictor, AdaBoost Learner is introduced to predict protein subcellular localization based on its amino acid composition. Jackknife cross-validation and independent dataset test were used to demonstrate that Adaboost is a robust and efficient model in predicting protein subcellular localization. As a result, the correct prediction rates were 74.98% and 80.12% for the Jackknife test and independent dataset test respectively, which are higher than using other existing predictors. An online server for predicting subcellular localization of proteins based on AdaBoost classifier was available on http://chemdata.shu. edu.cn/sl12.     

PredSL： A Tool for the N-terminal Sequence-based Prediction of Protein Subcellular Localization

The ability to predict the subcellular localization of a protein from its sequence is of great importance, as it provides information about the protein＇s function. We present a computational tool, PredSL, which utilizes neural networks, Markov chains, profile hidden Markov models, and scoring matrices for the prediction of the subcellular localization of proteins in eukaryotic cells from the N-terminal amino acid sequence. It aims to classify proteins into five groups： chloroplast, thylakoid, mitochondrion, secretory pathway, and ＂other＂. When tested in a fivefold cross-validation procedure, PredSL demonstrates 86.7% and 87.1% overall accuracy for the plant and non-plant datasets, respectively. Compared with TargetP, which is the most widely used method to date, and LumenP, the results of PredSL are comparable in most cases. When tested on the experimentally verified proteins of the Saccharomyces cerevisiae genome, PredSL performs comparably if not better than any available algorithm for the same task. Furthermore, PredSL is the only method capable for the prediction of these subcellular localizations that is available as a stand-alone application through the URL： http：//bioinformatics.biol.uoa.gr/PredSL/.     

Expression and subcellular localization of a novel nuclear acetylcholinesterase protein

Acetylcholine is found in the nervous system and also in other cell types (endothelium, lymphocytes, and epithelial and blood cells), which are globally termed the non-neuronal cholinergic system. In this study we investigated the expression and subcellular localization of acetylcholinesterase (AChE) in endothelial cells. Our results show the expression of the 70-kDa AChE in both cytoplasmic and nuclear compartments. We also describe, for the first time, a nuclear and cytoskeleton-bound AChE isoform with approximately 55 kDa detected in endothelial cells. This novel isoform is decreased in response to vascular endothelial growth factor via the proteosomes pathway, and it is down-regulated in human leukemic T-cells as compared with normal T-cells, suggesting that the decreased expression of the 55-kDa AChE protein may contribute to an angiogenic response and associate with tumorigenesis. Importantly, we show that its nuclear expression is not endothelial cell-specific but also evidenced in non-neuronal and neuronal cells. Concerning neuronal cells, we can distinguish an exclusively nuclear expression in postnatal neurons in contrast to a cytoplasmic and nuclear expression in embryonic neurons, suggesting that the cell compartmentalization of this new AChE isoform is changed during the development of nervous system. Overall, our studies suggest that the 55-kDa AChE may be involved in different biological processes such as neural development, tumor progression, and angiogenesis.     

Proteolytic activity assayed by subcellular localization switching of a substrate

An approach to assay proteolytic activity in vivo by altering the subcellular localization of a labelled substrate was demonstrated. The assay included a protein shuttling between different cellular compartments and a site-specific recombinant protease. The shuttle protein used was the human immunodeficiency virus type 1 (HIV-1) Rev protein tandemly fused to the enhanced green fluorescent protein (EGFP) and the red fluorescent protein (RFP), while the protease was the site-specific protease VP24 from the herpes simplex virus type 1 (HSV-1). The fluorescent proteins in the Rev fusion protein were separated by a cleavage site specific for the VP24 protease. When co-expressed in COS-7 cells proteolysis was observed by fluorescence microscopy as a shift from a predominantly cytoplasmic localization of the fusion protein RevEGFP to a nuclear localization while the RFP part of the fusion protein remained in the cytoplasm. The cleavage of the fusion protein by VP24 was confirmed by Western blot analysis. The activity of VP24, when tagged N-terminally by the Myc-epitope, was found to be comparable to VP24. These results demonstrates that the activity and localization of a recombinantly expressed protease can be assessed by protease-mediated cleavage of fusion proteins containing a specific protease cleavage site.