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1.
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/.  相似文献   

2.
Protein S-glutathionylation, the reversible formation of a mixed-disulfide between glutathione and protein thiols, is involved in protection of protein cysteines from irreversible oxidation, but also in protein redox regulation. Recent studies have implicated S-glutathionylation as a cellular response to oxidative/nitrosative stress, likely playing an important role in signaling. Considering the potential importance of glutathionylation, a number of methods have been developed for identifying proteins undergoing glutathionylation. These methods, ranging from analysis of purified proteins in vitro to large-scale proteomic analyses in vivo, allowed identification of nearly 200 targets in mammals. By contrast, the number of known glutathionylated proteins is more limited in photosynthetic organisms, although they are severely exposed to oxidative stress. The aim of this review is to detail the methods available for identification and analysis of glutathionylated proteins in vivo and in vitro. The advantages and drawbacks of each technique will be discussed as well as their application to photosynthetic organisms. Furthermore, an overview of known glutathionylated proteins in photosynthetic organisms is provided and the physiological importance of this post-translational modification is discussed.  相似文献   

3.
There are approximately 109 proteins in a cell. A hotspot in bioinformatics is how to identify a protein's subcellular localization, if its sequence is known. In this paper, a method using fast Fourier transform-based support vector machine is developed to predict the subcellular localization of proteins from their physicochemical properties and structural parameters. The prediction accuracies reached 83% in prokaryotic organisms and 84% in eukaryotic organisms with the substitution model of the c-p-v matrix (c, composition; p, polarity; and v, molecular volume). The overall prediction accuracy was also evaluated using the "leave-one-out" jackknife procedure. The influence of the substitution model on prediction accuracy has also been discussed in the work. The source code of the new program is available on request from the authors.  相似文献   

4.
Ni CZ  Wang HQ  Xu T  Qu Z  Liu GQ 《Cell research》2005,15(9):725-733
Kinesins and kinesin-like proteins (KLPs) constitute a large family of microtubule-based motors that play important roles in many fundamental cellular and developmental processes. To date, a number of kinesins or KLPs have been identified in plants including Arabidopsis thaliana. Here, a polyclonal antibody against AtKP1 (kinesin-like protein 1 in A. thaliana) was raised by injection the expressed AtKP1 specific C-terminal polypeptides in rabbits, and immunoblot analysis was conducted with the affinity-purified anti-AtKP1 antibody. The results indicated that this antibody recognized the AtKP1 fusion proteins expressed in E. coli and proteins of -125 kDa in the soluble fractions of Arabidopsis extracts. The molecular weight was consistent with the calculated molecular weight based on deduced amino acids sequence of AtKP1. To acquire the subcellular localization of the protein, AtKP1 in Arabidopsis root cells was observed by indirect immunofluorescence microscopy. AtKP1 was localized to particle-like organelles in interphase or dividing cells, but not to mitotic microtubule arrays. Relatively more AtKP1 was found in isolated mitochondria fraction on immunoblot of the subcellular fractions. The AtKP1 protein could not be released following a 0.6 M KI washing, indicating that AtKP1 is tightly bind to mitochondria and might function associated with this kind of organelles.  相似文献   

5.
The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.  相似文献   

6.
Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis (EDA) techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences (average Precision=0.88; average Sensitivity=0.86). Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized to the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.  相似文献   

7.
Lipid droplets (LDs) were once viewed as simple, inert lipid micelles. However, they are now known to be organelles with a rich proteome involved in a myriad of cellular processes. LDs are heterogeneous in nature with different sizes and compositions of phospholipids, neutral lipids and proteins. This review takes a focused look at the roles of proteins involved in the regulation of LD formation, expansion, and morphology. The related proteins are summarized such as the fat-specific protein (Fsp27), fat storage-inducing trans- membrane (FIT) proteins, seipin and ADP-ribosylation factor 1-coat protein complex I (Arf-COPI). Finally, we present important challenges in LD biology for a deeper understanding of this dynamic organelle to be achieved.  相似文献   

8.
Protein acetylation is one of the most abundant post translational modifications and plays critical roles in many important biological processes. Based on the recent advances in mass spectrometry technology, in bacteria, such as Escherichia coli, tremendous acetylated proteins and acetylation sites have been identified. However, only one protein deacetylase, i.e. CobB, has been identified in E. coli so far. How CobB is regulated is still elusive. One right strategy to study the regulation of CobB is to globally identify its interacting proteins. In this study, we used a proteome microarray containing 4000 affinitypurified E. coli proteins to globally identify CobB interactors, and finally identified 183 binding proteins of high stringency. Bioinformatics analysis showed that these interacting pro teins play a variety of roles in a wide range of cellular func tions and are highly enriched in carboxylic acid metabolic process and hexose catabolic process, and also enriched in transferase and hydrolase. We further used biolayer inter ferometry to analyze the interaction and quantify the kinetic parameters of putative CobB interactors, and clearly showed that Cobb could strongly interact with TopA and AccC. The novel CobB interactors that we identified could serve as a start point for further functional analysis.  相似文献   

9.
Selective degradation of the IκB kinase (IKK) by autophagy   总被引:1,自引:0,他引:1  
Li D 《Cell research》2006,16(11):855-856
Proteasome-mediated degradation and autophagy are the two major pathways mediating the turnover of cellular proteins. The proteasomal pathway is known to be a highly specific and regulated process mediating the degradation of short-lived proteins such as many important factors involved in cellular signaling. In contrast, it is generally thought that autophagy is rather nonselective as it is responsible for the bulk degradation of long-lived proteins and organelles. Challenging this general view, in this issue of Cell Research, Qing et al. report that selective degradation of the IκB kinase (IKK) triggered by the loss of Hsp90 function is mediated by autophagy [1].  相似文献   

10.
The Borfl protein is encoded by an immediate-early gene of the bovine foamy virus (BFV) and plays a key role in the viral life cycle. Borfl is a DNA binding protein which can transactivate both the long terminal repeat (LTR) and the internal promoter (IP) of BFV by specifically binding to the transactivation responsive element (TRE). To analyze the subcellular localization of Borfl during the BFV life cycle, this gene was cloned into a prokaryotic expression vector and expressed in a soluble form. After the purification and immunization, we raised the mouse anti-Borfl serum with a high titer based on ELISA results. Western blot analysis showed that the antiserum could specifically recognize the Borfl protein that was expressed in 293T cells. With this specific serum, we revealed the nuclear and cytoplasmic localization of Borfl in HeLa cells that was transfected with Borfl. Moreover, the immuno-fluorescence assay also showed that the localization of Borfl during the infection and transfection of BFV was identical.  相似文献   

11.
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.  相似文献   

12.
Eukaryotic cells consist of numerous membrane-bound organelles,which compartmentalize cellular materials to fulfil a variety of vital functions.In the post-genomic era,it is widely recognized that identification of the subcellular organelle localization and transport mechanisms of the encoded proteins are necessary for a fundamental understanding of their biological functions and the organization of cellular activity.Multiple experimental approaches are now available to determine the subcellular localizations and dynamics of proteins.In this review,we provide an overview of the current methods and organelle markers for protein subcellular localization and trafficking studies in plants,with a focus on the organelles of the endomembrane system.We also discuss the limitations of each method in terms of protein colocalization studies.  相似文献   

13.
In eukaryotic cells, a major proportion of the cellular proteins localize to various subcellular organelles where they are involved in organelle-specific cellular processes. Thus, the localization of a particular protein in the cell is an important part of understanding the physiological role of the protein in the cell. Various approaches such as subcellular fractionation, immunolocalization and live imaging have been used to define the localization of organellar proteins. Of these various approaches, the most powerful one is the live imaging because it can show in vivo dynamics of protein localization depending on cellular and environmental conditions without disturbing cellular structures. However, the live imaging requires the ability to detect the organelles in live cells. In this study, we report generation of a new set of transgenic Arabidopsis plants using various organelle marker proteins fused to a fluorescence protein, monomeric Cherry (mCherry). All these markers representing different subcellular organelles such as chloroplasts, mitochondria, peroxisomes, endoplasmic reticulum (ER) and lytic vacuole showed clear and specific signals regardless of the cell types and tissues. These marker lines can be used to determine localization of organellar proteins by colocalization and also to study the dynamics of organelles under various developmental and environmental conditions.  相似文献   

14.
Automated sequence annotation is a major goal of post-genomic era with hundreds of genomes in the databases, from both prokaryotes and eukaryotes. While the number of fully sequenced chromosomes from microbial organisms exponentially increased in the last decade above 600, presently we know the whole DNA content of only 25 eukaryotic organisms, including Homo sapiens. However, the process of genome annotation is far from being completed. This is particularly relevant in eukaryotes, whose cells contain several subcellular compartments, or organelles, enclosed by membranes, where different relevant functions are performed. Translocation across the membrane into the organelles is a highly regulated and complex cellular process. Indeed different proteins and/or protein isoforms, originated from genes by alternative splicing, may be conveyed to different cell compartments, depending on their specific role in the cell. During recent years the prediction of subcellular localization (SL) by computational means has been an active research area. Several methods are presently available based on different notions and addressing different aspects of SL. This review provides a short overview of the most well performing methods described in the literature, highlighting their predictive capabilities and different applications.  相似文献   

15.
It is well known that protein subcellular localizations are closely related to their functions. Although many computational methods and tools are available from Internet, it is still necessary to develop new algorithms in this filed to gain a better understanding of the complex mechanism of plant subcellular localization. Here, we provide a new web server named PSCL for plant protein subcellular localization prediction by employing optimized functional domains. After feature optimization, 848 optimal functional domains from InterPro were obtained to represent each protein. By calculating the distances to each of the seven categories, PSCL showing the possibilities of a protein located into each of those categories in ascending order. Toward our dataset, PSCL achieved a first-order predicted accuracy of 75.7% by jackknife test. Gene Ontology enrichment analysis showing that catalytic activity, cellular process and metabolic process are strongly correlated with the localization of plant proteins. Finally, PSCL, a Linux Operate System based web interface for the predictor was designed and is accessible for public use at http://pscl.biosino.org/.  相似文献   

16.
ABSTRACT: BACKGROUND: Understanding protein subcellular localization is a necessary component toward understanding the overall function of a protein. Numerous computational methods have been published over the past decade, with varying degrees of success. Despite the large number of published methods in this area, only a small fraction of them are available for researchers to use in their own studies. Of those that are available, many are limited by predicting only a small number of major organelles in the cell. Additionally, the majority of methods predict only a single location, even though it is known that a large fraction of the proteins in eukaryotic species shuttle between locations to carry out their function. FINDINGS: We present a software package and a web server for predicting subcellular localization of protein sequences based on the ngLOC method. ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 89.8% to 91.4% across species. This program can predict 11 distinct locations each in plant and animal species. ngLOC also predicts 4 and 5 distinct locations on gram-positive and gram-negative bacterial datasets, respectively. CONCLUSIONS: ngLOC is a generic method that can be trained by data from a variety of species or classes for predicting protein subcellular localization. The standalone software is freely available for academic use under GNU GPL, and the ngLOC web server is also accessible at http://ngloc.unmc.edu.  相似文献   

17.
Immunofluorescence microscopy is a valuable tool for analyzing protein expression and localization at a subcellular level thus providing information regarding protein function, interaction partners and its role in cellular processes. When performing sample fixation, parameters such as difference in accessibility of proteins present in various cellular compartments as well as the chemical composition of the protein to be studied, needs to be taken into account. However, in systematic and proteome-wide efforts, a need exists for standard fixation protocol(s) that works well for the majority of all proteins independent of subcellular localization. Here, we report on a study with the goal to find a standardized protocol based on the analysis of 18 human proteins localized in 11 different organelles and subcellular structures. Six fixation protocols were tested based on either dehydration by alcohols (methanol, ethanol or iso-propanol) or cross-linking by paraformaldehyde followed by detergent permeabilization (Triton X-100 or saponin) in three human cell lines. Our results show that cross-linking is essential for proteome-wide localization studies and that cross-linking using paraformaldehyde followed by Triton X-100 permeabilization successfully can be used as a single fixation protocol for systematic studies.  相似文献   

18.
Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computational methods for mRNA subcellular localization prediction provide a useful approach for studying mRNA functions. However, few computational methods were designed for mRNA subcellular localization prediction and their performance have room for improvement. Especially, there is still no available tool to predict for mRNAs that have multiple localization annotations. In this paper, we propose a multi-head self-attention method, DM3Loc, for multi-label mRNA subcellular localization prediction. Evaluation results show that DM3Loc outperforms existing methods and tools in general. Furthermore, DM3Loc has the interpretation ability to analyze RNA-binding protein motifs and key signals on mRNAs for subcellular localization. Our analyses found hundreds of instances of mRNA isoform-specific subcellular localizations and many significantly enriched gene functions for mRNAs in different subcellular localizations.  相似文献   

19.
Lee J  Lee H  Kim J  Lee S  Kim DH  Kim S  Hwang I 《The Plant cell》2011,23(4):1588-1607
Proteins localized to various cellular and subcellular membranes play pivotal roles in numerous cellular activities. Accordingly, in eukaryotic cells, the biogenesis of organellar proteins is an essential process requiring their correct localization among various cellular and subcellular membranes. Localization of these proteins is determined by either cotranslational or posttranslational mechanisms, depending on the final destination. However, it is not fully understood how the targeting specificity of membrane proteins is determined in plant cells. Here, we investigate the mechanism by which signal-anchored (SA) proteins are differentially targeted to the endoplasmic reticulum (ER) or endosymbiotic organelles using in vivo targeting, subcellular fractionation, and bioinformatics approaches. For targeting SA proteins to endosymbiotic organelles, the C-terminal positively charged region (CPR) flanking the transmembrane domain (TMD) is necessary but not sufficient. The hydrophobicity of the TMD in CPR-containing proteins also plays a critical role in determining targeting specificity; TMDs with a hydrophobicity value >0.4 on the Wimley and White scale are targeted primarily to the ER, whereas TMDs with lower values are targeted to endosymbiotic organelles. Based on these data, we propose that the CPR and the hydrophobicity of the TMD play a critical role in determining the targeting specificity between the ER and endosymbiotic organelles.  相似文献   

20.
Mei S 《PloS one》2012,7(6):e37716
Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar.  相似文献   

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