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.     

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.     

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.     

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.     

Cell growth-dependent subcellular localization of p8

p8 is a stress-induced protein, biochemically related to the architectural factor HMG-I/Y, overexpressed in many cancers and required for tumor expansion. The molecular mechanisms by which p8 may exert its effect in aspects of growth is unknown. Using immunocytochemistry, we found that p8 presents nuclear localization in sub-confluent cells, but it localizes throughout the whole cell in high density grown cells. Cells arrested in Go/G1, either by serum deprivation or by hydroxyurea treatment, show a nucleo-cytoplasmic localization of p8, whether in the rest of the cell cycle stages of actively dividing cells the localization is nuclear. A comparison of p8 sequences from human to fly predicts a conserved bipartite nuclear localization sequence (NLS). The putative NLS has been demonstrated to be functional, since nuclear import is energy dependent (inhibited by sodium azide plus 2-deoxyglucose), and fusion proteins GFP-p8 and GFP-NLSp8 localize to the nucleus, whereas GFP-p8NLSmut in which with Lys 65, 69, 76, and 77 mutated to Ala localized to the whole cell. p8 localization does not involve the CRM1 transporter, since it is insensitive to leptomycin B. Inhibitors of MAPK pathways did not affect p8 subcellular localization. The inhibition of deacetylation with Trichostatin A promotes cytoplasmic accumulation of p8. The results suggest that p8 growth stage-dependent localization is regulated by acetylation, that p8 is not free within the cell but forming part of a complex and that it may exert a role in both subcellular localizations.     

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.     

Cellular and subcellular localization of phototropin 1

Phototropin 1 (phot1) is a Ser/Thr photoreceptor kinase that binds two molecules of flavin mononucleotide as its chromophores and undergoes autophosphorylation in response to blue light. Phot1 is plasma membrane associated and, as with phot2, has been shown to function as a photoreceptor for phototropism, blue light-induced chloroplast movement, and blue light-induced stomatal opening. Phot1 likely also plays a redundant role with phot2 in regulating the rate of leaf expansion. Understanding the mechanism(s) by which phot1 initiates these four different responses requires, at minimum, knowledge of where the photoreceptor is located. Therefore, we transformed a phot1 null mutant of Arabidopsis with a construct encoding translationally fused phot1-green fluorescent protein (GFP) under the control of the endogenous PHOT1 promoter and investigated its cellular and subcellular distribution. This PHOT1-GFP construct complements the mutant phenotype, restoring second positive curvature. Phot1 is expressed strongly in dividing and elongating cortical cells in the apical hook and in the root elongation zone in etiolated seedlings. It is localized evenly to the plasma membrane region in epidermal cells but is confined largely to the plasma membrane region of the transverse cell walls in the cortical cells of both root and hypocotyl. It is found at both apical and basal ends of these cortical cells. In light-grown plants, phot1-GFP is localized largely in the plasma membrane regions adjacent to apical and basal cell end walls in the elongating inflorescence stem, where the photoreceptor is expressed strongly in the vascular parenchyma and leaf vein parenchyma. Phot1 also is localized to the plasma membrane region of leaf epidermal cells, mesophyll cells, and guard cells, where its distribution is uniform. Although phot1 is localized consistently to the plasma membrane region in etiolated seedlings, a fraction becomes released to the cytoplasm in response to blue light. Possible relationships between observed phot1 distribution and the various physiological responses activated by blue light are discussed.     

On the subcellular localization of the polyamines

Putrescine, spermidine and spermine were determined in the nuclear fraction of rat liver which was obtained by density gradient centrifugation in non-aqueous media, i.e. under conditions which avoid migration of water-soluble compounds. Calculations of the distribution of the polyamines between nuclear and extranuclear compartments were based on the assumption that the DNA is concentrated in the nuclei. No significant losses of the polyamines occurred during fractionation. From the polyamine determination in tissue and nuclear fraction it appeared that 16-17% of the liver spermidine and spermine, and about 8% of the putrescine content was localized in the nuclei. The spermidine/spermine-ratios in nuclei and whole tissue were not significantly different. Pretreatment of the animals with inhibitors of ornithine decarboxylase caused a decrease of putrescine exclusively in the extranuclear compartments, in agreement with a higher proportion of the inhibitors in the cytoplasm. Since the nuclear volume of rat liver corresponds to about 5% of total liver volume, the concentration of spermidine and spermine is higher in the nucleus than in extranuclear compartments. Published histochemical localizations of the polyamines suggested very low polyamine concentrations in the nuclei of non-dividing liver and HeLa cells, but dramatic polyamine accumulations in metaphase and anaphase nuclei. These results are in disagreement with previously reported autoradiographic data, subcellular localizations based on density gradient centrifugations, and with our present results. Since subcellular localization is a key issue in all attempts to clarify cellular functions of the polyamines the careful revision of the techniques involved in subcellular polyamine localizations seems imperative.     

Methods for predicting bacterial protein subcellular localization

The computational prediction of the subcellular localization of bacterial proteins is an important step in genome annotation and in the search for novel vaccine or drug targets. Since the 1991 release of PSORT I--the first comprehensive algorithm to predict bacterial protein localization--many other localization prediction tools have been developed. These methods offer significant improvements in predictive performance over PSORT I and the accuracy of some methods now rivals that of certain high-throughput laboratory methods for protein localization identification.     

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.     

BaCelLo: a balanced subcellular localization predictor

MOTIVATION: The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects. RESULTS: BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. AVAILABILITY: BaCelLo can be accessed at http://www.biocomp.unibo.it/bacello/.     

Prediction of subcellular localization using sequence-biased recurrent networks

MOTIVATION: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. RESULTS: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively. AVAILABILITY: The Protein Prowler incorporating the recurrent network predictor described in this paper is available online at http://pprowler.imb.uq.edu.au/     

Enzymatic properties and subcellular localization of Arabidopsis beta-N-acetylhexosaminidases

Plant glycoproteins contain substantial amounts of paucimannosidic N-glycans lacking terminal GlcNAc residues at their nonreducing ends. It has been proposed that this is due to the action of beta-hexosaminidases during late stages of N-glycan processing or in the course of N-glycan turnover. We have now cloned the three putative beta-hexosaminidase sequences present in the Arabidopsis (Arabidopsis thaliana) genome. When heterologously expressed as soluble forms in Spodoptera frugiperda cells, the enzymes (termed HEXO1-3) could all hydrolyze the synthetic substrates p-nitrophenyl-2-acetamido-2-deoxy-beta-d-glucopyranoside, p-nitrophenyl-2-acetamido-2-deoxy-beta-d-galactopyranoside, 4-methylumbelliferyl-2-acetamido-2-deoxy-beta-d-glucopyranoside, and 4-methylumbelliferyl-6-sulfo-2-acetamido-2-deoxy-beta-d-glucopyranoside, albeit to a varying extent. HEXO1 to HEXO3 were further able to degrade pyridylaminated chitotriose, whereas pyridylaminated chitobiose was only cleaved by HEXO1. With N-glycan substrates, HEXO1 displayed a much higher specific activity than HEXO2 and HEXO3. Nevertheless, all three enzymes were capable of removing terminal GlcNAc residues from the alpha1,3- and alpha1,6-mannosyl branches of biantennary N-glycans without any strict branch preference. Subcellular localization studies with HEXO-fluorescent protein fusions transiently expressed in Nicotiana benthamiana plants showed that HEXO1 is a vacuolar protein. In contrast, HEXO2 and HEXO3 are mainly located at the plasma membrane. These results indicate that HEXO1 participates in N-glycan trimming in the vacuole, whereas HEXO2 and/or HEXO3 could be responsible for the processing of N-glycans present on secretory glycoproteins.