Localization Of Gram-negative Bacterial Proteins Research Articles

The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone

The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone

The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone

Recently a very powerful web-server has been developed for predicting the subcellular localization of Gram-negative bacterial proteins purely according to their sequences information for the multi-label systems, in which a same protein may appear or move between two or more location sites and hence its ID (identification) needs two or more labels for distinction, namely the “multi-label mark”. The web-server is called as “pLoc_bal-mGneg”, where “bal” means that the predictor has been treated by balancing or quasi-balancing out the training dataset [3-9], and “m” means that the predictor is with the capacity to study the multi-label systems.

PLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC

One of the hottest topics in molecular cell biology is to determine the subcellular localization of proteins from various different organisms. This is because it is crucially important for both basic research and drug development. Recently, a predictor called “pLoc-mGneg” was developed for identifying the subcellular localization of Gram-negative bacterial proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mGneg was trained by an extremely skewed dataset in which some subset (subcellular location) was about 5 to 70 times the size of the other subsets. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To alleviate such a consequence, we have developed a new and bias-reducing predictor called pLoc_bal-mGneg by quasi-balancing the training dataset. Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mGneg, the existing state-of-the-art predictor in identifying the subcellular localization of Gram-negative bacterial proteins. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mGneg/, by which users can easily get their desired results without the need to go through the detailed mathematics.

Using Semi-supervised Discriminant Analysis to Predict Subcellular Localization of Gram-negative Bacterial Proteins

In this paper, an effective dimension reduction approach called semi-supervised discriminant analysis (SDA) is employed to deal with the protein subcellular localization problem. Firstly, a novel protein sequence encoding method that consists of pseudo amino acid composition (PseAAC) and dipeptide composition (DC) is introduced to represent a protein. Secondly, the SDA algorithm is applied to extract the essential discriminant features from the combined feature data set consisting of PseAAC and DC. Finally, the K-nearest neighbor (K-NN) classifier is used to identify the subcellular localization of Gram-positive bacterial proteins. The proposed method can effective utilize both manifold information and the class information of the protein samples to guide the produce of protein subcellular localization. To evaluate the prediction performance of the proposed algorithm, a jackknife test based on nearest neighbor algorithm is employed on the gram-negative bacterial proteins data set. The results show that we can get a high total accuracy in a low-dimensional feature space, which indicates that the proposed approach is effective and practical.

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.

Prediction subcellular localization of Gram-negative bacterial proteins by support vector machine using wavelet denoising and Chou's pseudo amino acid composition

Information on the subcellular localization of Gram-negative bacterial proteins is of great significance to study the pathogenesis, drug design and discovery of certain diseases. Protein subcellular localization is an important part of proteomics, while providing new opportunities and challenges for chemometrics. Since the prediction of protein subcellular localization can help to understand their function and the role played by their metabolic processes, a number of protein subcellular localization prediction methods have been developed in recent years. In this paper, we propose a novel method by combining wavelet denoising with support vector machine to predict the subcellular localization of proteins for the first time. Firstly, the features of the protein sequence are extracted by Chou's pseudo amino acid composition (PseAAC), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the Gram-negative bacterial proteins. Quite promising predictions are obtained using the jackknife test and compared with other predictive methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of protein subcellular localization, and it can be used to predict the other attributes of proteins.

Compressed Learning and Its Applications to Subcellular Localization

One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

A Multi-Label Classifier for Predicting the Subcellular Localization of Gram-Negative Bacterial Proteins with Both Single and Multiple Sites

Prediction of protein subcellular localization is a challenging problem, particularly when the system concerned contains both singleplex and multiplex proteins. In this paper, by introducing the “multi-label scale” and hybridizing the information of gene ontology with the sequential evolution information, a novel predictor called iLoc-Gneg is developed for predicting the subcellular localization of Gram-positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gneg-mPLoc was adopted to demonstrate the power of iLoc-Gneg. The dataset contains 1,392 Gram-negative bacterial proteins classified into the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. Of the 1,392 proteins, 1,328 are each with only one subcellular location and the other 64 are each with two subcellular locations, but none of the proteins included has pairwise sequence identity to any other in a same subset (subcellular location). It was observed that the overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gneg was over 91%, which is about 6% higher than that by Gneg-mPLoc. As a user-friendly web-server, iLoc-Gneg is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Gneg. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Gneg web-server also has the function to accept the batch job submission, which is not available in the existing version of Gneg-mPLoc web-server. It is anticipated that iLoc-Gneg may become a useful high throughput tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development.

Subcellular Localization of Gram-Negative Bacterial Proteins Using Sparse Learning

One of the main challenges faced by biological applications is to predict protein subcellular localization in an automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological system into account. Traditionally such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressive Sensing), we propose a method which performs locality preserving projection with a sparseness criterion such that the feature selection and dimension reduction are merged into one analysis. The proposed sparse method decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse method is quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

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/.

Predicting Subcellular Localization of Gram-Negative Bacterial Proteins by Linear Dimensionality Reduction Method

With the rapid increase of protein sequences in the post-genomic age, the need for an automated and accurate tool to predict protein subcellular localization becomes increasingly important. Many efforts have been tried. Most of them aim to find the optimal classification scheme and less of them take the simplifying the complexity of biological system into consideration. This work shows how to decrease the complexity of biological system with linear DR (Dimensionality Reduction) method by transforming the original high-dimensional feature vectors into the low-dimensional feature vectors. A powerful sequence encoding scheme by fusing PSSM (Position-Specific Score Matrix) and Chou's PseAA (Pseudo Amino Acid) composition is proposed to represent the protein samples. Then, the K-NN (K-Nearest Neighbor) classifier is employed to identify the subcellular localization based on their reduced low-dimensional feature vectors. Experimental results thus obtained are quite encouraging, indicating that the aforementioned linear DR method is quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.

Using the nonlinear dimensionality reduction method for the prediction of subcellular localization of Gram-negative bacterial proteins

One of the central problems in computational biology is protein function identification in an automated fashion. A key step to achieve this is predicting to which subcellular location the protein belongs, since protein localization correlates closely with its function. A wide variety of methods for protein subcellular localization prediction have been proposed over recent years. Linear dimensionality reduction (DR) methods have been introduced to address the high-dimensionality problem by transforming the representation of protein sequences. However, this approach is not suitable for some complex biological systems that have nonlinear characteristics. Herein, we use nonlinear DR methods such as the kernel DR method to capture the nonlinear characteristics of a high-dimensional space. Then, the K-nearest-neighbor (K-NN) classifier is employed to identify the subcellular localization of Gram-negative bacterial proteins based on their reduced low-dimensional features. Experimental results thus obtained are quite encouraging, indicating that the applied nonlinear DR method is effective to deal with this complicated problem of predicting subcellular localization of Gram-negative bacterial proteins. An online web server for predicting subcellular location of Gram-negative bacterial proteins is available at (http://202.120.37.185:8080/).

PSLpred: prediction of subcellular localization of bacterial proteins

We developed a web server PSLpred for predicting subcellular localization of gram-negative bacterial proteins with an overall accuracy of 91.2%. PSLpred is a hybrid approach-based method that integrates PSI-BLAST and three SVM modules based on compositions of residues, dipeptides and physico-chemical properties. The prediction accuracies of 90.7, 86.8, 90.3, 95.2 and 90.6% were attained for cytoplasmic, extracellular, inner-membrane, outer-membrane and periplasmic proteins, respectively. Furthermore, PSLpred was able to predict approximately 74% of sequences with an average prediction accuracy of 98% at RI = 5. PSLpred is available at http://www.imtech.res.in/raghava/pslpred/