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.     

Identification of the subcellular localization of mycobacterial proteins using localization motifs

Mycobacterium, the most common disease-causing genus, infects billions of people and is notoriously difficult to treat. Understanding the subcellular localization of mycobacterial proteins can provide essential clues for protein function and drug discovery. In this article, we present a novel approach that focuses on local sequence information to identify localization motifs that are generated by a merging algorithm and are selected based on a binomially distributed model. These localization motifs are employed as features for identifying the subcellular localization of mycobacterial proteins. Our approach provides more accurate results than previous methods and was tested on an independent dataset recently obtained from an experimental study to provide a first and reasonably accurate prediction of subcellular localization. Our approach can also be used for large-scale prediction of new protein entries in the UniportKB database and of protein sequences obtained experimentally. In addition, our approach identified many local motifs involved with the subcellular localization that also interact with the environment. Thus, our method may have widespread applications both in the study of the functions of mycobacterial proteins and in the search for a potential vaccine target for designing drugs.     

Predicting subcellular localization of proteins using machine-learned classifiers

MOTIVATION: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. RESULTS: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.     

Validating subcellular localization prediction tools with mycobacterial proteins

Background  

The 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.     

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 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.     

Characterization of human proteins with different subcellular localizations by topological and biological properties

Knowing the protein localization can provide valuable information resource for elucidating protein function. In recent years, with the advances of human genomics and proteomics, it is possible to characterize human proteins that are located in different subcellular localizations. In this study, we used the topological properties and biological properties to characterize human proteins with six subcellular localizations. Almost all of these properties were found to be significantly different among six protein categories. Network topology analysis indicated that several significant topological properties, including the degree and k-core, were higher for the mitochondrial proteins. Biological property analysis showed that the nuclear proteins appeared to be correlated with important biological function. We hope these findings may provide some important help for comprehensive understanding the biological function of proteins, and prediction of protein subcellular localizations in human.     

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.     

Detection and subcellular localization of two Sym plasmid-dependent proteins of Rhizobium leguminosarum biovar viciae.

The previously described Sym plasmid-dependent 24-kilodalton rhi protein of Rhizobium leguminosarum biovar viciae was localized in the cytosol fraction. Another Sym plasmid-dependent protein of 50 kilodaltons is secreted into the growth medium, and its expression is dependent on both the nodD gene and a nod gene inducer.     

Secretion, subcellular localization and metabolic status of inorganic pyrophosphate in human platelets. A major constituent of the amine-storing granules.

The platelet content of PPi is 1.90 +/- mumol/10(11) platelets (S.E.M., n = 19) or about 10.5 nmol/mg of protein, several hundred times that found for rat liver. Some 80% of this PPi is secreted by platelets treated with thrombin with a time course and dose-response relationship similar to secretion of ATP, ADP and 5-hydroxytryptamine (serotonin) from the platelet dense granules. During platelet aggregation induced by ADP and adrenaline, substantial amounts of PPi were secreted, but no release of acid hydrolases was observed. Subcellular-fractionation studies showed that the PPi is highly enriched in the same fraction that contains the storage organelles which store ATP, ADP, Ca2+ and 5-hydroxytryptamine. Inorganic pyrophosphatase was present mainly in the soluble fraction and in the mitochondria. Secretion studies done with platelets prelabelled with [32P]Pi showed that the sequestered PPi was relatively metabolically inactive, as is the ATP and ADP in the storage organelles. The possible participation of PPi in the formation of a bivalent-cation-nucleotide complex associated with amine storage is discussed.     

Expression and subcellular localization of Spred proteins in mouse and human tissues

Spred-1 and Spred-2 (Sprouty-related protein with an EVH1 domain) are recently described members of the EVH1 (Ena/VASP-homology domain 1) family. Both Spred-1 and Spred-2 are membrane-associated substrates of receptor tyrosine kinases and they act as negative regulators of the Ras pathway upon growth factor stimulation. Since the Spred family members seem to exert overlapping molecular functions, the isotype-specific function of each member remains enigmatic. To date, no comprehensive expression profiling of Spred proteins has been shown. Therefore, we compared mRNA and protein expression patterns of Spred-1 and Spred-2 systematically in mouse organs. Furthermore, we focused on the tissue-specific expression of Spred-2 in adult human tissues, the subcellular localization, and the potential role of Spred-2 in the organism. Our studies show that expression patterns of Spred-1 and Spred-2 differ markedly among various tissues and cell types. In mouse, Spred-1 and Spred-2 were found to be expressed predominantly in brain, whereas Spred-2 was found to be more widely expressed in various adult tissues than Spred-1. In humans, Spred-2 was found to be strongly expressed in glandular epithelia and, at the subcellular level, its immunoreactivity was associated with secretory vesicles. Using confocal microscopy we found Spred-2 to be strongly colocalized with Rab11 and, to a lesser extent, with Rab5a GTPase, an observation that was not made for Spred-1. We conclude that the two members of the recently discovered Spred protein family, Spred-1 and Spred-2, show a highly specific expression pattern in various tissues reflecting a specific physiological role for the individual Spred isoforms in these tissues. Furthermore, it becomes most likely that Spred-2 is involved in the regulation of secretory pathways.     

Nonlinear mechanical responses of mouse cochlear hair bundles.

The stiffness of sensory hair bundles of both inner (IHC) and outer (OHC) hair cells was measured with calibrated silica fibres in mouse cochlear cultures to test the hypothesis that the mechanical properties of the hair bundle reflect processes underlying mechanotransduction. For OHCs, the displacement of the hair bundle relaxed with time constants of 6 ms for displacements which open transducer channels and 4 ms for displacements which close the channels. The corresponding values of the time constants for IHCs were 10 ms and 8 ms, respectively. A displacement-dependent change in the stiffness of the hair bundle was not observed when the bundle was displaced orthogonally to the direction of excitation. The stiffness of the hair bundle as a function of nanometre displacements from the resting position was remarkably nonlinear. The stiffness declined to a minimum from the resting stiffness by about 12% for OHCs and 20% for IHCs when the hair bundle was displaced by about 20 nm in the excitatory direction, and it increased by a similar amount when the bundle was displaced by 20 nm in the inhibitory direction. The displacement at which the stiffness reached a minimum was within the most sensitive region of the hair-cell transducer function (receptor potential as a function of hair-bundle displacement), and the displacement at which the stiffness reached a maximum was at the point of saturation of the transducer function in the inhibitory direction. The nonlinear displacement-dependent compliance change is reversibly abolished, and the time constant of relaxation of the bundle for excitatory displacements is reversibly reduced, when mechanotransduction is blocked by the addition of either neomycin sulphate or cobalt chloride to the solution bathing the hair cells. The displacement-dependent compliance change was not apparently reduced when the receptor potential was attenuated through the substitution of sodium in the bathing solution with a less permeant cation, tetraethylammonium. These findings suggest that the nonlinear mechanical properties of the hair bundle are associated with aspects of the hair-cell mechanotransducer process. The mechanical properties of the hair bundle are discussed in relation to the 'gating-spring' hypothesis of hair-cell transduction.     

A family of RS domain proteins with novel subcellular localization and trafficking

We report the sequence, conservation and cell biology of a novel protein, Psc1, which is expressed and regulated within the embryonic pluripotent cell population of the mouse. The Psc1 sequence includes an RS domain and an RNA recognition motif (RRM), and a sequential arrangement of protein motifs that has not been demonstrated for other RS domain proteins. This arrangement was conserved in a second mouse protein (BAC34721). The identification of Psc1 and BAC34721 homologues in vertebrates and related proteins, more widely throughout evolution, defines a new family of RS domain proteins termed acidic rich RS (ARRS) domain proteins. Psc1 incorporated into the nuclear speckles, but demonstrated novel aspects of subcellular distribution including localization to speckles proximal to the nuclear periphery and localization to punctate structures in the cytoplasm termed cytospeckles. Integration of Psc1 into cytospeckles was dependent on the RRM. Cytospeckles were dynamic within the cytoplasm and appeared to traffic into the nucleus. These observations suggest a novel role in RNA metabolism for ARRS proteins.     

Generation of optimized yellow and red fluorescent proteins with distinct subcellular localization

Fluorescent proteins (FPs) have revolutionized many aspects of cell biology and have become indispensable research tools. Today's increasingly complex experiments aiming to understand biological systems strongly depend on the availability of combinations of multiple FPs, which allow their distinguishable simultaneous detection in the same cell or tissue. Recently, the VENUS and DsRed. T4 FPs were described as the latest generation of yellow and red FPs. To increase the combinatorial possibilities when using these optimized FPs, we have generated and successfully tested seven new forms of VENUS and DsRed. T4 proteins with distinct subcellular localization. To facilitate their use as markers in biological experiments, bicistronic expression constructs, which have been optimized for robust expression in almost all mammalian developmental stages and cell types, were produced for the new FPs. In addition, several plasmids were created, which contain all necessary elements for inserting the reading frames of these FPs into specific gene loci in knock-in experiments without disrupting the reading frame of the endogenous gene.