WoLF PSORT: protein localization predictor

WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.or

A bootstrap-based linear classifier fusion system for protein subcellular location prediction

The subcellular location plays a pivotal role in the functionality of proteins. In this paper we develop a multi-stage linear classifier fusion system based on Efron's bootstrap sampling for predicting subcellular locations of yeast proteins. Three different types of classifiers, i.e. the Naive Bayes (NB) classifier, Radial Basis Function (RBF) network, and Multilayer Perceptron (MLP), are utilized to construct the component modules in the fusion system. Ten bootstrapped instance sets are generated for training each type of component classifiers respectively. The linear fusion models, updated by the Least-Mean-Square (LMS) algorithm, are used to integrate the local decisions of the component classifiers and derive the final predictions. The empirical results show that the RBF classifiers can reach at slightly higher accuracy and better precision versus the NB or MLP ones. The linear fusion system consistently improves the overall prediction accuracy, in particular 6.65%, 1.77%, and 3.21%, superior to the NB, REF, and MLP component classifiers, respectively

A Folding Pathway-Dependent Score to Recognize Membrane Proteins

While various approaches exist to study protein localization, it is still a challenge to predict where proteins localize. Here, we consider a mechanistic viewpoint for membrane localization. Taking into account the steps for the folding pathway of α-helical membrane proteins and relating biophysical parameters to each of these steps, we create a score capable of predicting the propensity for membrane localization and call it FP3mem. This score is driven from the principal component analysis (PCA) of the biophysical parameters related to membrane localization. FP3mem allows us to rationalize the colocalization of a number of channel proteins with the Cav1.2 channel by their fewer propensities for membrane localization

Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods

The subcellular localization of a protein can provide important information about its function within the cell. As eukaryotic cells and particularly mammalian cells are characterized by a high degree of compartmentalization, most protein activities can be assigned to particular cellular compartments. The categorization of proteins by their subcellular localization is therefore one of the essential goals of the functional annotation of the human genome. We previously performed a subcellular localization screen of 52 proteins encoded on human chromosome 21. In the current study, we compared the experimental localization data to the in silico results generated by nine leading software packages with different prediction resolutions. The comparison revealed striking differences between the programs in the accuracy of their subcellular protein localization predictions. Our results strongly suggest that the recently developed predictors utilizing multiple prediction methods tend to provide significantly better performance over purely sequence-based or homology-based predictions

LocateP: Genome-scale subcellular-location predictor for bacterial proteins

Contains fulltext : 69477.pdf ( ) (Open Access)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

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

Comparative molecular, physiological and proteomic analyses of maize and sorghum subjected to water deficit stress

>Magister Scientiae - MScDrought is a major abiotic stress which causes not only differences between the mean yield and the potential yield but also yield variation from year to year. Although selection for genotypes with improved productivity under drought environments has been a central goal of numerous plant breeding programs, the molecular basis for plant tolerance towards drought stress is still poorly understood. Exposure of plants to this abiotic stress is known to trigger excessive formation of reactive oxygen species (ROS), which induce cell death and reduce growth. Part of the mechanism of plant responses to drought involves alterations in the expression of antioxidant enzymes and biosynthesis of different compatible solutes such as proline. Sorghum is regarded as generally more drought tolerant than maize, and it is a potential key model system for investigating the physiological and molecular mechanisms conferring drought tolerance. Comparative studies in crop plants to decipher differences in drought tolerance are essential for crop improvement to sustain a higher level of production, which in turn will improve food security, under severe drought conditions resulting from climate change. On this basis, the aim of this study is to determine molecular differences between Zea mays and Sorghum bicolor in response to drought stress in an attempt to identify novel biomarkers for drought tolerance. The physiological and molecular responses of maize and sorghum were studied for changes in growth, chlorophyll content, relative water content, ROS content, lipid peroxidation level, proline content, and antioxidant enzyme activity. Spectral Count Label-free Quantitation analysis was conducted to reveal the changes in protein profiles under drought in attempt to identify drought-responsive molecular mechanisms in the leaves of the two plant species. In this study, water deficit triggered mechanisms that resulted in overproduction of ROS in both Zea mays and Sorghum bicolor. However, Sorghum bicolor showed less oxidative damage under water stress compared to Zea mays. Drought-induced proline accumulation in the roots of Sorghum bicolor was associated with enhanced water retention. Significant changes were identified in the antioxidant enzyme activity between the two plant species in response to drought conditions. Proteomics results showed differing patterns for drought-responsive proteins in the two species. Together with the physiological, biochemical and proteomic profiling results between Zea mays and Sorghum bicolor, potential proteins and/or metabolic pathways underlying drought tolerance were identified. The findings obtained through this study provide insight towards understanding the molecular basis of crop drought tolerance