PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria

Automated prediction of bacterial protein subcellular localization is an important tool for genome annotation and drug discovery. PSORT has been one of the most widely used computational methods for such bacterial protein analysis; however, it has not been updated since it was introduced in 1991. In addition, neither PSORT nor any of the other computational methods available make predictions for all five of the localization sites characteristic of Gram-negative bacteria. Here we present PSORT-B, an updated version of PSORT for Gram-negative bacteria, which is available as a web-based application at http://www.psort.org. PSORT-B examines a given protein sequence for amino acid composition, similarity to proteins of known localization, presence of a signal peptide, transmembrane alpha-helices and motifs corresponding to specific localizations. A probabilistic method integrates these analyses, returning a list of five possible localization sites with associated probability scores. PSORT-B, designed to favor high precision (specificity) over high recall (sensitivity), attained an overall precision of 97% and recall of 75% in 5-fold cross-validation tests, using a dataset we developed of 1443 proteins of experimentally known localization. This dataset, the largest of its kind, is freely available, along with the PSORT-B source code (under GNU General Public License).     

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.     

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.     

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.     

A mechanism for polar protein localization in bacteria

We investigate a mechanism for the polar localization of proteins in bacteria. We focus on the MinCD/DivIVA system regulating division site placement in the rod-shaped bacterium Bacillus subtilis. Our model relies on a combination of geometric effects and reaction-diffusion dynamics to direct proteins to both cell poles, where division is then blocked. We discuss similarities and differences with related division models in Escherichia coli and also develop extensions of the model to asymmetric polar protein localization. We propose that our mechanism for polar localization may be employed more widely in bacteria, especially in outgrowing spores, which do not possess any pre-existing polar division apparatus from prior division events.     

Gneg-mPLoc: A top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins

By incorporating the information of gene ontology, functional domain, and sequential evolution, a new predictor called Gneg-mPLoc was developed. It can be used to identify Gram-negative bacterial proteins among the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. It can also be used to deal with the case when a query protein may simultaneously exist in more than one location. Compared with the original predictor called Gneg-PLoc, the new predictor is much more powerful and flexible. For a newly constructed stringent benchmark dataset in which none of proteins included has ≥25% pairwise sequence identity to any other in a same subset (location), the overall jackknife success rate achieved by Gneg-mPLoc was 85.5%, which was more than 14% higher than the corresponding rate by the Gneg-PLoc. As a user friendly web-server, Gneg-mPLoc is freely accessible at http://www.csbio.sjtu.edu.cn/bioinf/Gneg-multi/.     

PROlocalizer: integrated web service for protein subcellular localization prediction

Subcellular localization is an important protein property, which is related to function, interactions and other features. As experimental determination of the localization can be tedious, especially for large numbers of proteins, a number of prediction tools have been developed. We developed the PROlocalizer service that integrates 11 individual methods to predict altogether 12 localizations for animal proteins. The method allows the submission of a number of proteins and mutations and generates a detailed informative document of the prediction and obtained results. PROlocalizer is available at .     

Discriminative motif finding for predicting protein subcellular localization

Many methods have been described to predict the subcellular location of proteins from sequence information. However, most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify potential annotation errors in public databases for the location of some of the proteins. A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/.     

Outer membrane protein biogenesis in Gram-negative bacteria

Gram-negative bacteria contain a double membrane which serves for both protection and for providing nutrients for viability. The outermost of these membranes is called the outer membrane (OM), and it contains a host of fully integrated membrane proteins which serve essential functions for the cell, including nutrient uptake, cell adhesion, cell signalling and waste export. For pathogenic strains, many of these outer membrane proteins (OMPs) also serve as virulence factors for nutrient scavenging and evasion of host defence mechanisms. OMPs are unique membrane proteins in that they have a β-barrel fold and can range in size from 8 to 26 strands, yet can still serve many different functions for the cell. Despite their essential roles in cell survival and virulence, the exact mechanism for the biogenesis of these OMPs into the OM has remained largely unknown. However, the past decade has witnessed significant progress towards unravelling the pathways and mechanisms necessary for moulding a nascent polypeptide into a functional OMP within the OM. Here, we will review some of these recent discoveries that have advanced our understanding of the biogenesis of OMPs in Gram-negative bacteria, starting with synthesis in the cytoplasm to folding and insertion into the OM.     

Multi-label multi-kernel transfer learning for human protein subcellular localization

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.     

Gene ontology based transfer learning for protein subcellular localization

Background  

Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as GO, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the GO terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.     

TESTLoc: protein subcellular localization prediction from EST data

Background  

The eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes.     

Support vector machine approach for protein subcellular localization prediction

MOTIVATION: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. AVAILABILITY: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.     

A mechanism of protein localization: the signal hypothesis and bacteria

We are studying the molecular mechanism of cellular protein localization. The availability of genetic techniques, such as gene fusion in Escherichia coli, has made this problem particularly amenable to study in this prokaryote. We have constructed a variety of strains in which the gene coding for an outer membrane protein is fused to the gene coding for a normally cytoplasmic enzyme, beta-galactosidase. The hybrid proteins produced by such strains retain beta-galactosidase activity; this activity serves as a simple biochemical tag for studying the localization of the outer membrane protein. In addition, we have exploited phenotypes exhibited by certain fusion strains to isolate mutants that are altered in the process of protein export. Genetic and biochemical analyses of such mutants have provided evidence that the molecular mechanism of cellular protein localization is strinkingly similar in both bacteria and animal cells.     

A novel representation for apoptosis protein subcellular localization prediction using support vector machine

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.     

PredSL： A Tool for the N-terminal Sequence-based Prediction of Protein Subcellular Localization

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/.     

Meta-prediction of protein subcellular localization with reduced voting

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 × 10⁻⁶, Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.     

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.     

蛋白质亚细胞定位预测中的序列编码技术

蛋白质序列的编码是亚细胞定位预测问题中的关键技术之一。该文较为详细地介绍了目前已有的蛋白质序列编码算法;并指出了序列编码中存在的一些问题及可能的发展方向。