Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites

One of the critical challenges in predicting protein subcellular localization is how to deal with the case of multiple location sites. Unfortunately, so far, no efforts have been made in this regard except for the one focused on the proteins in budding yeast only. For most existing predictors, the multiple-site proteins are either excluded from consideration or assumed even not existing. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. For instance, according to the Swiss-Prot database (version 50.7, released 19-Sept-2006), among the 33,925 eukaryotic protein entries that have experimentally observed subcellular location annotations, 2715 have multiple location sites, meaning about 8% bearing the multiplex feature. Proteins with multiple locations or dynamic feature of this kind are particularly interesting because they may have some very special biological functions intriguing to investigators in both basic research and drug discovery. Meanwhile, according to the same Swiss-Prot database, the number of total eukaryotic protein entries (except those annotated with "fragment" or those with less than 50 amino acids) is 90,909, meaning a gap of (90,909-33,925) = 56,984 entries for which no knowledge is available about their subcellular locations. Although one can use the computational approach to predict the desired information for the blank, so far, all the existing methods for predicting eukaryotic protein subcellular localization are limited in the case of single location site only. To overcome such a barrier, a new ensemble classifier, named Euk-mPLoc, was developed that can be used to deal with the case of multiple location sites as well. Euk-mPLoc is freely accessible to the public as a Web server at http://202.120.37.186/bioinf/euk-multi. Meanwhile, to support the people working in the relevant areas, Euk-mPLoc has been used to identify all eukaryotic protein entries in the Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results thus obtained have been deposited at the same Web site via a downloadable file prepared with Microsoft Excel and named "Tab_Euk-mPLoc.xls". Furthermore, to include new entries of eukaryotic proteins and reflect the continuous development of Euk-mPLoc in both the coverage scope and prediction accuracy, we will timely update the downloadable file as well as the predictor, and keep users informed by publishing a short note in the Journal and making an announcement in the Web Page.     

Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites

Proteins may simultaneously exist at, or move between, two or more different subcellular locations. Proteins with multiple locations or dynamic feature of this kind are particularly interesting because they may have some very special biological functions intriguing to investigators in both basic research and drug discovery. For instance, among the 6408 human protein entries that have experimentally observed subcellular location annotations in the Swiss-Prot database (version 50.7, released 19-Sept-2006), 973 ( approximately 15%) have multiple location sites. The number of total human protein entries (except those annotated with "fragment" or those with less than 50 amino acids) in the same database is 14,370, meaning a gap of (14,370-6408)=7962 entries for which no knowledge is available about their subcellular locations. Although one can use the computational approach to predict the desired information for the gap, so far all the existing methods for predicting human protein subcellular localization are limited in the case of single location site only. To overcome such a barrier, a new ensemble classifier, named Hum-mPLoc, was developed that can be used to deal with the case of multiple location sites as well. Hum-mPLoc is freely accessible to the public as a web server at http://202.120.37.186/bioinf/hum-multi. Meanwhile, for the convenience of people working in the relevant areas, Hum-mPLoc has been used to identify all human protein entries in the Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results thus obtained have been deposited in a downloadable file prepared with Microsoft Excel and named "Tab_Hum-mPLoc.xls". This file is available at the same website and will be updated twice a year to include new entries of human proteins and reflect the continuous development of Hum-mPLoc.     

Large-scale predictions of gram-negative bacterial protein subcellular locations

Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells. Therefore, developing an automated method for fast and reliable prediction of Gram-negative protein subcellular location will allow us to not only timely annotate gene products, but also screen candidates for drug discovery. However, protein subcellular location prediction is a very difficult problem, particularly when more location sites need to be involved and when unknown query proteins do not have significant homology to proteins of known subcellular locations. PSORT-B, a recently updated version of PSORT, widely used for predicting Gram-negative protein subcellular location, only covers five location sites. Also, the data set used to train PSORT-B contains many proteins with high degrees of sequence identity in a same location group and, hence, may bear a strong homology bias. To overcome these problems, a new predictor, called "Gneg-PLoc", is developed. Featured by fusing many basic classifiers each being trained with a stringent data set containing proteins with strictly less than 25% sequence identity to one another in a same location group, the new predictor can cover eight subcellular locations; that is, cytoplasm, extracellular space, fimbrium, flagellum, inner membrane, nucleoid, outer membrane, and periplasm. In comparison with PSORT-B, the new predictor not only covers more subcellular locations, but also yields remarkably higher success rates. Gneg-PLoc is available as a Web server at http://202.120.37.186/bioinf/Gneg. To support the demand of people working in the relevant areas, a downloadable file is provided at the same Web site to list the results identified by Gneg-PLoc for 49 907 Gram-negative protein entries in the Swiss-Prot database that have no subcellular location annotations or are annotated with uncertain terms. The large-scale results will be updated twice a year to cover the new entries of Gram-negative bacterial proteins and reflect the new development of Gneg-PLoc.     

Large-scale plant protein subcellular location prediction

Current plant genome sequencing projects have called for development of novel and powerful high throughput tools for timely annotating the subcellular location of uncharacterized plant proteins. In view of this, an ensemble classifier, Plant-PLoc, formed by fusing many basic individual classifiers, has been developed for large-scale subcellular location prediction for plant proteins. Each of the basic classifiers was engineered by the K-Nearest Neighbor (KNN) rule. Plant-PLoc discriminates plant proteins among the following 11 subcellular locations: (1) cell wall, (2) chloroplast, (3) cytoplasm, (4) endoplasmic reticulum, (5) extracell, (6) mitochondrion, (7) nucleus, (8) peroxisome, (9) plasma membrane, (10) plastid, and (11) vacuole. As a demonstration, predictions were performed on a stringent benchmark dataset in which none of the proteins included has > or =25% sequence identity to any other in a same subcellular location to avoid the homology bias. The overall success rate thus obtained was 32-51% higher than the rates obtained by the previous methods on the same benchmark dataset. The essence of Plant-PLoc in enhancing the prediction quality and its significance in biological applications are discussed. Plant-PLoc is accessible to public as a free web-server at: (http://202.120.37.186/bioinf/plant). Furthermore, for public convenience, results predicted by Plant-PLoc have been provided in a downloadable file at the same website for all plant protein entries in the Swiss-Prot database that do not have subcellular location annotations, or are annotated as being uncertain. The large-scale results will be updated twice a year to include new entries of plant proteins and reflect the continuous development of Plant-PLoc.     

Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction

With the avalanche of newly-found protein sequences emerging in the post genomic era, it is highly desirable to develop an automated method for fast and reliably identifying their subcellular locations because knowledge thus obtained can provide key clues for revealing their functions and understanding how they interact with each other in cellular networking. However, predicting subcellular location of eukaryotic proteins is a challenging problem, particularly when unknown query proteins do not have significant homology to proteins of known subcellular locations and when more locations need to be covered. To cope with the challenge, protein samples are formulated by hybridizing the information derived from the gene ontology database and amphiphilic pseudo amino acid composition. Based on such a representation, a novel ensemble hybridization classifier was developed by fusing many basic individual classifiers through a voting system. Each of these basic classifiers was engineered by the KNN (K-Nearest Neighbor) principle. As a demonstration, a new benchmark dataset was constructed that covers the following 18 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cyanelle, (5) cytoplasm, (6) cytoskeleton, (7) endoplasmic reticulum, (8) extracell, (9) Golgi apparatus, (10) hydrogenosome, (11) lysosome, (12) mitochondria, (13) nucleus, (14) peroxisome, (15) plasma membrane, (16) plastid, (17) spindle pole body, and (18) vacuole. To avoid the homology bias, none of the proteins included has > or =25% sequence identity to any other in a same subcellular location. The overall success rates thus obtained via the 5-fold and jackknife cross-validation tests were 81.6 and 80.3%, respectively, which were 40-50% higher than those performed by the other existing methods on the same strict dataset. The powerful predictor, named "Euk-PLoc", is available as a web-server at http://202.120.37.186/bioinf/euk . Furthermore, to support the need of people working in the relevant areas, a downloadable file will be provided at the same website to list the results predicted by Euk-PLoc for all eukaryotic protein entries (excluding fragments) in Swiss-Prot database that do not have subcellular location annotations or are annotated as being uncertain. The large-scale results will be updated twice a year to include the new entries of eukaryotic proteins and reflect the continuous development of Euk-PLoc.     

Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm

Machine learning is a kind of reliable technology for automated subcellular localization of viral proteins within a host cell or virus-infected cell. One challenge is that the viral protein samples are not only with multiple location sites, but also class-imbalanced. The imbalanced dataset often decreases the prediction performance. In order to accomplish this challenge, this paper proposes a novel approach named imbalance-weighted multi-label K-nearest neighbor to predict viral protein subcellular location with multiple sites. The experimental results by jackknife test indicate that the presented algorithm achieves a better performance than the existing methods and has great potentials in protein science.     

Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis

Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.     

A comparative proteomic strategy for subcellular proteome research: ICAT approach coupled with bioinformatics prediction to ascertain rat liver mitochondrial proteins and indication of mitochondrial localization for catalase

Subcellular proteomics, as an important step to functional proteomics, has been a focus in proteomic research. However, the co-purification of "contaminating" proteins has been the major problem in all the subcellular proteomic research including all kinds of mitochondrial proteome research. It is often difficult to conclude whether these "contaminants" represent true endogenous partners or artificial associations induced by cell disruption or incomplete purification. To solve such a problem, we applied a high-throughput comparative proteome experimental strategy, ICAT approach performed with two-dimensional LC-MS/MS analysis, coupled with combinational usage of different bioinformatics tools, to study the proteome of rat liver mitochondria prepared with traditional centrifugation (CM) or further purified with a Nycodenz gradient (PM). A total of 169 proteins were identified and quantified convincingly in the ICAT analysis, in which 90 proteins have an ICAT ratio of PM:CM>1.0, while another 79 proteins have an ICAT ratio of PM:CM<1.0. Almost all the proteins annotated as mitochondrial according to Swiss-Prot annotation, bioinformatics prediction, and literature reports have a ratio of PM:CM>1.0, while proteins annotated as extracellular or secreted, cytoplasmic, endoplasmic reticulum, ribosomal, and so on have a ratio of PM:CM<1.0. Catalase and AP endonuclease 1, which have been known as peroxisomal and nuclear, respectively, have shown a ratio of PM:CM>1.0, confirming the reports about their mitochondrial location. Moreover, the 125 proteins with subcellular location annotation have been used as a testing dataset to evaluate the efficiency for ascertaining mitochondrial proteins by ICAT analysis and the bioinformatics tools such as PSORT, TargetP, SubLoc, MitoProt, and Predotar. The results indicated that ICAT analysis coupled with combinational usage of different bioinformatics tools could effectively ascertain mitochondrial proteins and distinguish contaminant proteins and even multilocation proteins. Using such a strategy, many novel proteins, known proteins without subcellular location annotation, and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location.     

A high-throughput approach for subcellular proteome: identification of rat liver proteins using subcellular fractionation coupled with two-dimensional liquid chromatography tandem mass spectrometry and bioinformatic analysis

Four fractions from rat liver (a crude mitochondria (CM) and cytosol (C) fraction obtained with differential centrifugation, a purified mitochondrial (PM) fraction obtained with nycodenz density gradient centrifugation, and a total liver (TL) fraction) were analyzed with two-dimensional liquid chromatography tandem mass spectrometry analysis. A total of 564 rat proteins were identified and were bioinformatically annotated according to their physicochemical characteristics and functions. While most extreme alkaline ribosomal proteins were identified in the TL fraction, the C fraction mainly included neutral enzymes and the PM fraction enriched alkaline proteins and proteins with electron transfer activity or oxygen binding activity. Such characteristics were more apparent in proteins identified only in the TL, C, or PM fraction. The Swiss-Prot annotation and the bioinformatic prediction results proved that the C and PM fractions had enriched cytoplasmic or mitochondrial proteins, respectively. Combination usage of subcellular fractionation with two-dimensional liquid chromatography tandem mass spectrometry was proved to be a high-throughput, sensitive, and effective analytical approach for subcellular proteomics research. Using such a strategy, we have constructed the largest proteome database to date for rat liver (564 rat proteins) and its cytosol (222 rat proteins) and mitochondrial fractions (227 rat proteins). Moreover, the 352 proteins with Swiss-Prot subcellular location annotation in the 564 identified proteins were used as an actual subcellular proteome dataset to evaluate the widely used bioinformatics tools such as PSORT, TargetP, TMHMM, and GRAVY.     

Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-Nearest Neighbor classifiers

Facing the explosion of newly generated protein sequences in the post genomic era, we are challenged to develop an automated method for fast and reliably annotating their subcellular locations. Knowledge of subcellular locations of proteins can provide useful hints for revealing their functions and understanding how they interact with each other in cellular networking. Unfortunately, it is both expensive and time-consuming to determine the localization of an uncharacterized protein in a living cell purely based on experiments. To tackle the challenge, a novel hybridization classifier was developed by fusing many basic individual classifiers through a voting system. The "engine" of these basic classifiers was operated by the OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) rule. As a demonstration, predictions were performed with the fusion classifier for proteins among the following 16 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cyanelle, (5) cytoplasm, (6) cytoskeleton, (7) endoplasmic reticulum, (8) extracell, (9) Golgi apparatus, (10) lysosome, (11) mitochondria, (12) nucleus, (13) peroxisome, (14) plasma membrane, (15) plastid, and (16) vacuole. To get rid of redundancy and homology bias, none of the proteins investigated here had >/=25% sequence identity to any other in a same subcellular location. The overall success rates thus obtained via the jack-knife cross-validation test and independent dataset test were 81.6% and 83.7%, respectively, which were 46 approximately 63% higher than those performed by the other existing methods on the same benchmark datasets. Also, it is clearly elucidated that the overwhelmingly high success rates obtained by the fusion classifier is by no means a trivial utilization of the GO annotations as prone to be misinterpreted because there is a huge number of proteins with given accession numbers and the corresponding GO numbers, but their subcellular locations are still unknown, and that the percentage of proteins with GO annotations indicating their subcellular components is even less than the percentage of proteins with known subcellular location annotation in the Swiss-Prot database. It is anticipated that the powerful fusion classifier may also become a very useful high throughput tool in characterizing other attributes of proteins according to their sequences, such as enzyme class, membrane protein type, and nuclear receptor subfamily, among many others. A web server, called "Euk-OET-PLoc", has been designed at http://202.120.37.186/bioinf/euk-oet for public to predict subcellular locations of eukaryotic proteins by the fusion OET-KNN classifier.     

Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms

Information on subcellular localization of proteins is important to molecular cell biology, proteomics, system biology and drug discovery. To provide the vast majority of experimental scientists with a user-friendly tool in these areas, we present a package of Web servers developed recently by hybridizing the 'higher level' approach with the ab initio approach. The package is called Cell-PLoc and contains the following six predictors: Euk-mPLoc, Hum-mPLoc, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively. Using these Web servers, one can easily get the desired prediction results with a high expected accuracy, as demonstrated by a series of cross-validation tests on the benchmark data sets that covered up to 22 subcellular location sites and in which none of the proteins included had > or =25% sequence identity to any other protein in the same subcellular-location subset. Some of these Web servers can be particularly used to deal with multiplex proteins as well, which may simultaneously exist at, or move between, two or more different subcellular locations. Proteins with multiple locations or dynamic features of this kind are particularly interesting, because they may have some special biological functions intriguing to investigators in both basic research and drug discovery. This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package. The computational time for each prediction is less than 5 s in most cases. The Cell-PLoc package is freely accessible at http://chou.med.harvard.edu/bioinf/Cell-PLoc.     

Yeast Protein database (YPD): a database for the complete proteome of Saccharomyces cerevisiae.

The Yeast Protein Database (YPD) is a database for the proteins of the budding yeast,Saccharomyces cerevisiae. YPD is the first annotated database for the complete proteome of any organism. Now that the complete genome sequence of yeast is available, YPD contains entries for each of the characterized proteins and for each of the uncharacterized proteins predicted from the sequence. Contained in YPD are the calculated properties of each protein such as molecular weight and isoelectric point, experimentally determined properties such as subcellular localization and post-translational modifications, and extensive annotations from the yeast literature. YPD contains 25 000 lines of textual annotation that describe the known functions, mutant phenotypes, interactions, and other properties for the approximately 6000 proteins in the yeast proteome. The information in YPD is updated daily, and it is available on the World Wide Web at http://www.proteome.com/YPDhome.html .     

Application of the accurate mass and time tag approach to the proteome analysis of sub-cellular fractions obtained from Rhodobacter sphaeroides 2.4.1. Aerobic and photosynthetic cell cultures

The high-throughput accurate mass and time (AMT) tag proteomic approach was utilized to characterize the proteomes for cytoplasm, cytoplasmic membrane, periplasm, and outer membrane fractions from aerobic and photosynthetic cultures of the gram-nagtive bacterium Rhodobacter sphaeroides 2.4.1. In addition, we analyzed the proteins within purified chromatophore fractions that house the photosynthetic apparatus from photosynthetically grown cells. In total, 8,300 peptides were identified with high confidence from at least one subcellular fraction from either cell culture. These peptides were derived from 1,514 genes or 35% percent of proteins predicted to be encoded by the genome. A significant number of these proteins were detected within a single subcellular fraction and their localization was compared to in silico predictions. However, the majority of proteins were observed in multiple subcellular fractions, and the most likely subcellular localization for these proteins was investigated using a Z-score analysis of estimated protein abundance along with clustering techniques. Good (81%) agreement was observed between the experimental results and in silico predictions. The AMT tag approach provides localization evidence for those proteins that have no predicted localization information, those annotated as putative proteins, and/or for those proteins annotated as hypothetical and conserved hypothetical.     

Intrinsic disorder in the Protein Data Bank

The Protein Data Bank (PDB) is the preeminent source of protein structural information. PDB contains over 32,500 experimentally determined 3-D structures solved using X-ray crystallography or nuclear magnetic resonance spectroscopy. Intrinsically disordered regions fail to form a fixed 3-D structure under physiological conditions. In this study, we compare the amino-acid sequences of proteins whose structures are determined by X-ray crystallography with the corresponding sequences from the Swiss-Prot database. The analyzed dataset includes 16,370 structures, which represent 18,101 PDB chains and 5,434 different proteins from 910 different organisms (2,793 eukaryotic, 2,109 bacterial, 288 viral, and 244 archaeal). In this dataset, on average, each Swiss-Prot protein is represented by 7 PDB chains with 76% of the crystallized regions being represented by more than one structure. Intriguingly, the complete sequences of only approximately 7% of proteins are observed in the corresponding PDB structures, and only approximately 25% of the total dataset have >95% of their lengths observed in the corresponding PDB structures. This suggests that the vast majority of PDB proteins is shorter than their corresponding Swiss-Prot sequences and/or contain numerous residues, which are not observed in maps of electron density. To determine the prevalence of disordered regions in PDB, the residues in the Swiss-Prot sequences were grouped into four general categories, "Observed" (which correspond to structured regions), "Not observed" (regions with missing electron density, potentially disordered), "Uncharacterized," and "Ambiguous," depending on their appearance in the corresponding PDB entries. This non-redundant set of residues can be viewed as a 'fragment' or empirical domain database that contains a set of experimentally determined structured regions or domains and a set of experimentally verified disordered regions or domains. We studied the propensities and properties of residues in these four categories and analyzed their relations to the predictions of disorder using several algorithms. "Non-observed," "Ambiguous," and "Uncharacterized" regions were shown to possess the amino acid compositional biases typical of intrinsically disordered proteins. The application of four different disorder predictors (PONDR(R) VL-XT, VL3-BA, VSL1P, and IUPred) revealed that the vast majority of residues in the "Observed" dataset are ordered, and that the "Not observed" regions are mostly disordered. The "Uncharacterized" regions possess some tendency toward order, whereas the predictions for the short "Ambiguous" regions are really ambiguous. Long "Ambiguous" regions (>70 amino acid residues) are mostly predicted to be ordered, suggesting that they are likely to be "wobbly" domains. Overall, we showed that completely ordered proteins are not highly abundant in PDB and many PDB sequences have disordered regions. In fact, in the analyzed dataset approximately 10% of the PDB proteins contain regions of consecutive missing or ambiguous residues longer than 30 amino-acids and approximately 40% of the proteins possess short regions (> or =10 and < 30 amino-acid long) of missing and ambiguous residues.     

Lipids as modulators of membrane fusion mediated by viral fusion proteins

Enveloped viruses infect host cells by fusion of viral and target membranes. This fusion event is triggered by specific glycoproteins in the viral envelope. Fusion glycoproteins belong to either class I, class II or the newly described third class, depending upon their arrangement at the surface of the virion, their tri-dimensional structure and the location within the protein of a short stretch of hydrophobic amino acids called the fusion peptide, which is able to induce the initial lipid destabilization at the onset of fusion. Viral fusion occurs either with the plasma membrane for pH-independent viruses, or with the endosomal membranes for pH-dependent viruses. Although, viral fusion proteins are parted in three classes and the subcellular localization of fusion might vary, these proteins have to act, in common, on lipid assemblies. Lipids contribute to fusion through their physical, mechanical and/or chemical properties. Lipids can thus play a role as chemically defined entities, or through their preferential partitioning into membrane microdomains called "rafts", or by modulating the curvature of the membranes involved in the fusion process. The purpose of this review is to make a state of the art on recent findings on the contribution of cholesterol, sphingolipids and glycolipids in cell entry and membrane fusion of a number of viral families, whose members bear either class I or class II fusion proteins, or fusion proteins of the recently discovered third class.     

Systematic analysis of Arabidopsis organelles and a protein localization database for facilitating fluorescent tagging of full-length Arabidopsis proteins

Cells are organized into a complex network of subcellular compartments that are specialized for various biological functions. Subcellular location is an important attribute of protein function. To facilitate systematic elucidation of protein subcellular location, we analyzed experimentally verified protein localization data of 1,300 Arabidopsis (Arabidopsis thaliana) proteins. The 1,300 experimentally verified proteins are distributed among 40 different compartments, with most of the proteins localized to four compartments: mitochondria (36%), nucleus (28%), plastid (17%), and cytosol (13.3%). About 19% of the proteins are found in multiple compartments, in which a high proportion (36.4%) is localized to both cytosol and nucleus. Characterization of the overrepresented Gene Ontology molecular functions and biological processes suggests that the Golgi apparatus and peroxisome may play more diverse functions but are involved in more specialized processes than other compartments. To support systematic empirical determination of protein subcellular localization using a technology called fluorescent tagging of full-length proteins, we developed a database and Web application to provide preselected green fluorescent protein insertion position and primer sequences for all Arabidopsis proteins to study their subcellular localization and to store experimentally verified protein localization images, videos, and their annotations of proteins generated using the fluorescent tagging of full-length proteins technology. The database can be searched, browsed, and downloaded using a Web browser at http://aztec.stanford.edu/gfp/. The software can also be downloaded from the same Web site for local installation.     

Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach

MOTIVATION: Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner. RESULTS: We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community. AVAILABILITY: We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).     

Seq2Struct: a resource for establishing sequence-structure links

SUMMARY: Several methods for establishing cross-links between Protein Data Bank (PDB) structures or Structural Classification of Proteins (SCOP) domains and Swiss-Prot + TrEMBL sequences (or vice versa) rely on database annotations. Alternatively, sequence alignment procedures can be used. In this study, we describe Seq2Struct, a web resource for the identification of sequence-structure links. The resource consists of an exhaustive collection of annotated links between Swiss-Prot + TrEMBL and PDB + SCOP database entries. Links are based on pre-established highly reliable thresholds and stored in a relational database, which has been enhanced using annotations derived from Swiss-Prot, PDB, SCOP, GOA and DSSP databases. The Seq2Struct database contents, supported by a WWW web interface, can be queried both online and downloaded. AVAILABILITY: The Seq2Struct resource, with related documentation, is available at http://surface.bio.uniroma2.it/seq2struct/ CONTACT: seq2struct@cbm.bio.uniroma2.it.     

UniProt蛋白质数据库简介

UniProt(https://www.uniprot.org/)是国际知名蛋白质数据库,主要包括UniProtKB知识库、UniParc归档库和UniRef参考序列集三部分。UniProtKB知识库是UniProt的核心,除蛋白质序列数据外,还包括大量注释信息。UniProtKB知识库分Swiss-Prot和TrEMBL两个子库。Swiss-Prot子库中50多万条序列均由人工审阅和注释,而TrEMBL子库中1.4亿多条序列是由核酸序列数据库EMBL中的蛋白质编码序列翻译所得,并由计算机根据一定规则进行注释。UniParc归档库将存放于不同数据库中的同一个蛋白质归并到一个记录中以避免冗余,并赋予序列唯一性特定标识符。UniRef参考序列集按相似性程度将UniProtKB和UniParc中的序列分为UniRef100、UniRef90和UniRef50三个数据集。UniProt网站为用户提供了高效实用的高级检索系统和大量帮助文档。UniProt数据库每4周发布新版的同时也发布统计报表,用户可通过统计报表了解该数据库的数据量及更新情况、数据类别和物种分布等基本信息,查看常规注释信息、序列特征注释信息和数据库交叉链接等统计数据。UniProt是目前国际上序列数据最完整、注释信息最丰富的非冗余蛋白质序列数据库,自本世纪初创建以来,为生命科学领域提供了宝贵资源。     

The Swiss-Prot protein knowledgebase and ExPASy: providing the plant community with high quality proteomic data and tools

The Swiss-Prot protein knowledgebase provides manually annotated entries for all species, but concentrates on the annotation of entries from model organisms to ensure the presence of high quality annotation of representative members of all protein families. A specific Plant Protein Annotation Program (PPAP) was started to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Its main goal is the annotation of proteins from the model plant organism Arabidopsis thaliana. In addition to bibliographic references, experimental results, computed features and sometimes even contradictory conclusions, direct links to specialized databases connect amino acid sequences with the current knowledge in plant sciences. As protein families and groups of plant-specific proteins are regularly reviewed to keep up with current scientific findings, we hope that the wealth of information of Arabidopsis origin accumulated in our knowledgebase, and the numerous software tools provided on the Expert Protein Analysis System (ExPASy) web site might help to identify and reveal the function of proteins originating from other plants. Recently, a single, centralized, authoritative resource for protein sequences and functional information, UniProt, was created by joining the information contained in Swiss-Prot, Translation of the EMBL nucleotide sequence (TrEMBL), and the Protein Information Resource-Protein Sequence Database (PIR-PSD). A rising problem is that an increasing number of nucleotide sequences are not being submitted to the public databases, and thus the proteins inferred from such sequences will have difficulties finding their way to the Swiss-Prot or TrEMBL databases.