Dataset Information

Protein-protein interaction as a predictor of subcellular location.

ABSTRACT: 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. RESULTS: Using publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence (empirical approach, multiplicity of databases, and multiplicity of literature citations) and three based on type of biological evidence (inferred biological process, domain-domain interactions, and orthology relationships), with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins. CONCLUSION: Understanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions.

SUBMITTER: Shin CJ

PROVIDER: S-EPMC2663780 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Protein-protein interaction as a predictor of subcellular location.

Shin Chang Jin CJ Wong Simon S Davis Melissa J MJ Ragan Mark A MA

BMC systems biology 20090225

<h4>Background</h4>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 sho ...[more]

PMID: 19243629

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Project description:BACKGROUND: In the past decades, various protein subcellular-location (SCL) predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase) cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP. RESULTS: LocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms current tools especially where the N-terminally anchored and the SPIase-cleaved secreted proteins are concerned. Overall, the accuracy of LocateP was always higher than 90%. LocateP was then used to predict the SCLs of all proteins encoded by completed Gram-positive bacterial genomes. The results are stored in the database LocateP-DB http://www.cmbi.ru.nl/locatep-db1. CONCLUSION: LocateP is by far the most accurate and detailed protein SCL predictor for Gram-positive bacteria currently available.

Project description:BACKGROUND:The effects of estrogens on dopamine (DA) transport may have important implications for the increased incidence of neurological disorders in women during life stages when hormonal fluctuations are prevalent, e.g. during menarche, reproductive cycling, pregnancy, and peri-menopause. RESULTS:The activity of the DA transporter (DAT) was measured by the specific uptake of 3H-DA. We found that low concentrations (10(-14) to 10(-8) M) of 17beta-estradiol (E2) inhibit uptake via the DAT in PC12 cells over 30 minutes, with significant inhibition taking place due to E2 exposure during only the last five minutes of the uptake period. Such rapid action suggests a non-genomic, membrane-initiated estrogenic response mechanism. DAT and estrogen receptor-alpha (ERalpha) were elevated in cell extracts by a 20 ng/ml 2 day NGFbeta treatment, while ERbeta was not. DAT, ERalpha and ERbeta were also detectable on the plasma membrane of unpermeabilized cells by immunocytochemical staining and by a fixed cell, quantitative antibody (Ab)-based plate assay. In addition, PC12 cells contained RNA coding for the alternative membrane ER GPR30; therefore, all 3 ER subtypes are candidates for mediating the rapid nongenomic actions of E2. At cell densities above 15,000 cells per well, the E2-induced inhibition of transport was reversed. Uptake activity oscillated with time after a 10 nM E2 treatment; in a slower room temperature assay, inhibition peaked at 9 min, while uptake activity increased at 3 and 20-30 min. Using an Ab recognizing the second extracellular loop of DAT (accessible only on the outside of unpermeabilized cells), our immunoassay measured membrane vs. intracellular/nonvesicular DAT; both were found to decline over a 5-60 min E2 treatment, though immunoblot analyses demonstrated no total cellular loss of protein. CONCLUSION:Our results suggest that physiological levels of E2 may act to sequester DAT in intracellular compartments where the transporter's second extramembrane loop is inaccessible (inside vesicles) and that rapid estrogenic actions on this differentiated neuronal cell type may be regulated via membrane ERs of several types.

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Protein-protein interaction as a predictor of subcellular location.

Publications

Protein-protein interaction as a predictor of subcellular location.

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