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.     

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.     

MetaLocGramN: A meta-predictor of protein subcellular localization for Gram-negative bacteria

Subcellular localization is a key functional characteristic of proteins. It is determined by signals encoded in the protein sequence. The experimental determination of subcellular localization is laborious. Thus, a number of computational methods have been developed to predict the protein location from sequence. However predictions made by different methods often disagree with each other and it is not always clear which algorithm performs best for the given cellular compartment. We benchmarked primary subcellular localization predictors for proteins from Gram-negative bacteria, PSORTb3, PSLpred, CELLO, and SOSUI-GramN, on a common dataset that included 1056 proteins. We found that PSORTb3 performs best on the average, but is outperformed by other methods in predictions of extracellular proteins. This motivated us to develop a meta-predictor, which combines the primary methods by using the logistic regression models, to take advantage of their combined strengths, and to eliminate their individual weaknesses. MetaLocGramN runs the primary methods, and based on their output classifies protein sequences into one of five major localizations of the Gram-negative bacterial cell: cytoplasm, plasma membrane, periplasm, outer membrane, and extracellular space. MetaLocGramN achieves the average Matthews correlation coefficient of 0.806, i.e. 12% better than the best individual primary method. MetaLocGramN is a meta-predictor specialized in predicting subcellular localization for proteins from Gram-negative bacteria. According to our benchmark, it performs better than all other tools run independently. MetaLocGramN is a web and SOAP server available for free use by all academic users at the URL http://iimcb.genesilico.pl/MetaLocGramN. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions

Gram-negative bacteria have five major subcellular localization sites: the cytoplasm, the periplasm, the inner membrane, the outer membrane, and the extracellular space. The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. We present an approach to predict subcellular localization for Gram-negative bacteria. This method uses the support vector machines trained by multiple feature vectors based on n-peptide compositions. For a standard data set comprising 1443 proteins, the overall prediction accuracy reaches 89%, which, to the best of our knowledge, is the highest prediction rate ever reported. Our prediction is 14% higher than that of the recently developed multimodular PSORT-B. Because of its simplicity, this approach can be easily extended to other organisms and should be a useful tool for the high-throughput and large-scale analysis of proteomic and genomic data.     

PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis

MOTIVATION: PSORTb v.1.1 is the most precise bacterial localization prediction tool available. However, the program's predictive coverage and recall are low and the method is only applicable to Gram-negative bacteria. The goals of the present work are as follows: increase PSORTb's coverage while maintaining the existing precision level, expand it to include Gram-positive bacteria and then carry out a comparative analysis of localization. RESULTS: An expanded database of proteins of known localization and new modules using frequent subsequence-based support vector machines was introduced into PSORTb v.2.0. The program attains a precision of 96% for Gram-positive and Gram-negative bacteria and predictive coverage comparable to other tools for whole proteome analysis. We show that the proportion of proteins at each localization is remarkably consistent across species, even in species with varying proteome size. AVAILABILITY: Web-based version: http://www.psort.org/psortb. Standalone version: Available through the website under GNU General Public License. CONTACT: psort-mail@sfu.ca, brinkman@sfu.ca SUPPLEMENTARY INFORMATION: http://www.psort.org/psortb/supplementaryinfo.html.     

Amino-terminal amino acid sequences of electron transfer proteins from Gram-negative bacteria as indicators of their cellular localization: the sulfate-reducing bacteria

Abstract Data are presented that all known periplasmic redox proteins from the sulfate reducing bacteria included in the genus, Desulfovibrio have aminoterminal (N-terminal) amino-acid sequences commonly found in other Gram-negative bacteria and are indicative of recognition sites for signal peptides. In contrast, none of the cytoplasmic redox proteins exhibited these unique N-terminal amino-acid sequences. It is proposed that the N-terminal amino-acid residues of a given protein can be used as an indicator of its cellular localization within the bacterial cell.     

Transit peptide cleavage sites of integral thylakoid membrane proteins

A set of 58 nuclearly encoded thylakoid-integral membrane proteins from four plant species was identified, and their amino termini were assigned unequivocally based upon mass spectrometry of intact proteins and peptide fragments. The dataset was used to challenge the Web tools ChloroP, TargetP, SignalP, PSORT, Predotar, and MitoProt II for predicting organelle targeting and transit peptide proteolysis sites. ChloroP and TargetP reliably predicted chloroplast targeting but only reliably predicted transit peptide cleavage sites for soluble proteins targeted to the stroma. SignalP (eukaryote settings) accurately predicted the transit peptide cleavage site for soluble proteins targeted to the lumen. SignalP (Gram-negative bacteria settings) reliably predicted peptide cleavage of integral thylakoid proteins inserted into the membrane via the "spontaneous" pathway. The processing sites of more common thylakoid-integral proteins inserted by the signal recognition peptide-dependent pathway were not well predicted by any of the programs. The results suggest the presence of a second thylakoid processing protease that recognizes the transit peptide of integral proteins inserted via the spontaneous mechanism and that this mechanism may be related to the secretory mechanism of Gram-negative bacteria.     

SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria

A predictive software system, SOSUI-GramN, was developed for assessing the subcellular localization of proteins in Gram-negative bacteria. The system does not require the sequence homology data of any known sequences; instead, it uses only physicochemical parameters of the N- and C-terminal signal sequences, and the total sequence. The precision of the prediction system for subcellular localization to extracellular, outer membrane, periplasm, inner membrane and cytoplasmic medium was 92.3%, 89.4%, 86.4%, 97.5% and 93.5%, respectively, with corresponding recall rates of 70.3%, 87.5%, 76.0%, 97.5% and 88.4%, respectively. The overall performance for precision and recall obtained using this method was 92.9% and 86.7%, respectively. The comparison of performance of SOSUI-GramN with that of other methods showed the performance of prediction for extracellular proteins, as well as inner and outer membrane proteins, was either superior or equivalent to that obtained with other systems. SOSUI-GramN particularly improved the accuracy for predictions of extracellular proteins which is an area of weakness common to the other methods.     

GNBSL: a new integrative system to predict the subcellular location for Gram-negative bacteria proteins

This paper proposes a new integrative system (GNBSL--Gram-negative bacteria subcellular localization) for subcellular localization specifized on the Gram-negative bacteria proteins. First, the system generates a position-specific frequency matrix (PSFM) and a position-specific scoring matrix (PSSM) for each protein sequence by searching the Swiss-Prot database. Then different features are extracted by four modules from the PSFM and the PSSM. The features include whole-sequence amino acid composition, N- and C-terminus amino acid composition, dipeptide composition, and segment composition. Four probabilistic neural network (PNN) classifiers are used to classify these modules. To further improve the performance, two modules trained by support vector machine (SVM) are added in this system. One module extracts the residue-couple distribution from the amino acid sequence and the other module applies a pairwise profile alignment kernel to measure the local similarity between every two sequences. Finally, an additional SVM is used to fuse the outputs from the six modules. Test on a benchmark dataset shows that the overall success rate of GNBSL is higher than those of PSORT-B, CELLO, and PSLpred. A web server GNBSL can be visited from http://166.111.24.5/webtools/GNBSL/index.htm.     

pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC

Information of the proteins' subcellular localization is crucially important for revealing their biological functions in a cell, the basic unit of life. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop computational tools for timely identifying their subcellular locations based on the sequence information alone. The current study is focused on the Gram-negative bacterial proteins. Although considerable efforts have been made in protein subcellular prediction, the problem is far from being solved yet. This is because mounting evidences have indicated that many Gram-negative bacterial proteins exist in two or more location sites. Unfortunately, most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions important for both basic research and drug design. In this study, by using the multi-label theory, we developed a new predictor called “pLoc-mGneg” for predicting the subcellular localization of Gram-negative bacterial proteins with both single and multiple locations. Rigorous cross-validation on a high quality benchmark dataset indicated that the proposed predictor is remarkably superior to “iLoc-Gneg”, the state-of-the-art predictor for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for the novel predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGneg/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.     

基于茶蛋白质组学数据分析植物亚细胞定位预测软件的应用

以500个茶（Camellia sinensis（L.）O.Ktze.）叶片的蛋白质作为数据集,比较TargetP、WoLF PSORT、LocTree和Plant-mPLoc 4种软件预测亚细胞定位的可信度和灵敏度。结果显示,4种软件预测可信度均高于80%,依次排序为TargetP > LocTree > WoLF PSORT > Plant-mPLoc。其中,LocTree对细胞质蛋白和分泌蛋白检测灵敏度最高,但对叶绿体蛋白灵敏度最低;Plant-mPLoc检测核蛋白最灵敏,但对细胞质蛋白最不敏感;TargetP检测叶绿体蛋白最灵敏,但仅能区分3个亚细胞器官;WoLF PSORT对分泌蛋白检测灵敏度最低,但对其他蛋白均较灵敏。基于上述结果,该研究针对4种软件提出了合理的使用建议。     

Genome-wide enzyme annotation with precision control: catalytic families (CatFam) databases

In this article, we present a new method termed CatFam (Catalytic Families) to automatically infer the functions of catalytic proteins, which account for 20-40% of all proteins in living organisms and play a critical role in a variety of biological processes. CatFam is a sequence-based method that generates sequence profiles to represent and infer protein catalytic functions. CatFam generates profiles through a stepwise procedure that carefully controls profile quality and employs nonenzymes as negative samples to establish profile-specific thresholds associated with a predefined nominal false-positive rate (FPR) of predictions. The adjustable FPR allows for fine precision control of each profile and enables the generation of profile databases that meet different needs: function annotation with high precision and hypothesis generation with moderate precision but better recall. Multiple tests of CatFam databases (generated with distinct nominal FPRs) against enzyme and nonenzyme datasets show that the method's predictions have consistently high precision and recall. For example, a 1% FPR database predicts protein catalytic functions for a dataset of enzymes and nonenzymes with 98.6% precision and 95.0% recall. Comparisons of CatFam databases against other established profile-based methods for the functional annotation of 13 bacterial genomes indicate that CatFam consistently achieves higher precision and (in most cases) higher recall, and that (on average) CatFam provides 21.9% additional catalytic functions not inferred by the other similarly reliable methods. These results strongly suggest that the proposed method provides a valuable contribution to the automated prediction of protein catalytic functions. The CatFam databases and the database search program are freely available at http://www.bhsai.org/downloads/catfam.tar.gz.     

Functional assignment to maize group 1 LEA protein EMB564 within the cell nucleus using computational analysis

Maize protein EMB564 is a member of group 1 LEA (late embryogenesis abundant) proteins. Currently, the molecular functions of group 1 LEA proteins remain largely unclear. We here report on the functional assignment to EMB564 by computational analysis. EMB564 is predicted as nuclear localization by five different predictors including CELLO, Plant-mPLoc, WoLF PSORT, Predotar and TargetP. EMB564 is found to be remote homologous with DNA/RNA helicases and single-stranded DNA-binding proteins, and their sequences contains similar DNA/RNA binding sites. Furthermore, the three-dimensional (3D) model of EMB564 structurally resembles a variety of nuclear and DNA/RNA-binding proteins, especially those involving in the regulation of cell division, chromosomal replication and DNA unwinding or repairing. Our results reveal that EMB564 protein is most likely to function within the cell nucleus.     

PSLDoc: Protein subcellular localization prediction based on gapped-dipeptides and probabilistic latent semantic analysis

Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836-2847; Yu et al., Proteins 2006;64:643-651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.21%, respectively, and it compares favorably with that of CELLO II (Yu et al., Proteins 2006;64:643-651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005;21:617-623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/~ bioapp/PSLDoc/.     

AutoMotif Server for prediction of phosphorylation sites in proteins using support vector machine: 2007 update

We present here the recent update of AutoMotif Server (AMS 2.0) that predicts post-translational modification sites in protein sequences. The support vector machine (SVM) algorithm was trained on data gathered in 2007 from various sets of proteins containing experimentally verified chemical modifications of proteins. Short sequence segments around a modification site were dissected from a parent protein, and represented in the training set as binary or profile vectors. The updated efficiency of the SVM classification for each type of modification and the predictive power of both representations were estimated using leave-one-out tests for model of general phosphorylation and for modifications catalyzed by several specific protein kinases. The accuracy of the method was improved in comparison to the previous version of the service (Plewczynski et al., “AutoMotif server: prediction of single residue post-translational modifications in proteins”, Bioinformatics 21: 2525–7, 2005). The precision of the updated version reached over 90% for selected types of phosphorylation and was optimized in trade of lower recall value of the classification model. The AutoMotif Server version 2007 is freely available at . Additionally, the reference dataset for optimization of prediction of phosphorylation sites, collected from the UniProtKB was also provided and can be accessed at .     

用离散增量结合支持向量机方法预测蛋白质亚细胞定位

对未知蛋白的功能注释是蛋白质组学的主要目标。一个关键的注释是蛋白质亚细胞定位的预测。本文应用离散增量结合支持向量机(ID_SVM)的方法,对阳性革兰氏细菌蛋白的5类亚细胞定位点进行预测。在独立检验下,其总体预测成功率为89.66%。结果发现ID_SVM算法对预测的成功率有很大改进。     

Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines

Background  

Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in Gram-negative bacteria obtained good results. However, these methods had relatively low accuracies for the localization of extracellular proteins. This paper studies ways to improve the accuracy for predicting extracellular localization in Gram-negative bacteria.     

The subcellular localization of a C-terminal processing protease in Pseudomonas aeruginosa

Carboxy (C)-terminal processing proteases (CTP) are a relatively new group of serine proteases. Found in a broad range of organisms - bacteria, archaea, algae, plants and animals - these proteases are involved in the C-terminal processing of proteins. In comparison with amino-terminal processing of bacterial proteins, less is known about C-terminal processing and its physiological function. Bacterial CTPs appear to influence different basal cellular processes. Although CTPs of Gram-negative bacteria are generally referred to as being localized in the periplasm, there is little experimental evidence for this. We show for the first time the subcellular localization of a CTP-3 family protein from Pseudomonas aeruginosa, named CtpA, in the periplasm by a carefully designed fractionation study. Our results provide experimental evidence for the generally accepted hypothesis that CTPs are located in the periplasmic space of Gram-negative bacteria.     

Predicting subcellular localization of proteins using machine-learned classifiers

MOTIVATION: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location. RESULTS: We have constructed five machine-learning classifiers for predicting subcellular localization of proteins from animals, plants, fungi, Gram-negative bacteria and Gram-positive bacteria, which are 81% accurate for fungi and 92-94% accurate for the other four categories. These are the most accurate subcellular predictors across the widest set of organisms ever published. Our predictors are part of the Proteome Analyst web-service.     

使用多特征联合变量的支持向量机方法预测外膜蛋白

外膜蛋白(Outer Membrane Proteins, OMPs)是一类具有重要生物功能的蛋白质, 通过生物信息学方法来预测OMPs能够为预测OMPs的二级和三级结构以及在基因组发现新的OMPs提供帮助。文中提出计算蛋白质序列的氨基酸含量特征、二肽含量特征和加权多阶氨基酸残基指数相关系数特征, 将三类特征组合, 采用支持向量机(Support Vector Machine, SVM)算法来识别OMPs。计算了包括四种残基指数的多种组合特征的识别结果, 并且讨论了相关系数的阶次和权值对预测性能的影响。在数据集上的十倍交叉验证测试和独立性测试结果显示, 组合特征识别方法对OMPs和非OMPs的识别精度最高分别达到96.96%和97.33%, 优于现有的多种方法。在五种细菌基因组内识别OMPs的结果显示, 组合特征方法具有很高的特异性, 并且对PDB数据库中已知结构的OMPs识别准确度超过99%。表明该方法能够作为基因组内筛选OMPs的有效工具。