Fast Fourier transform-based support vector machine for subcellular localization prediction using different substitution models

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

Bagging with CTD- A Novel Signature for the Hierarchical Prediction of Secreted Protein Trafficking in Eukaryotes

Protein trafficking or protein sorting in eukaryotes is a complicated process and is carried out based on the information contaified in the protein. Many methods reported prediction of the subcellular location of proteins from sequence information. However, most of these prediction methods use a flat structure or parallel architecture to perform prediction. In this work, we introduce ensemble classifiers with features that are extracted directly from full length protein sequences to predict locations in the protein-sorting pathway hierarchically. Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared. When evaluated by independent data testing, ensemble based-bagging algorithms with sequence feature composition, transition and distribution （CTD） successfully classified two datasets with accuracies greater than 90%. We compared our results with similar published methods, and our method equally performed with the others at two levels in the secreted pathway. This study shows that the feature CTD extracted from protein sequences is effective in capturing biological features among compartments in secreted pathways.     

Predicting protein subcellular location using digital signal processing

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

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis

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

Methods for Analysis of Protein Glutathionylation and their Application to Photosynthetic Organisms

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

DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning

Protein nitration and nitrosylation are essential post-translational modifications(PTMs)involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specificscoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.     

Identification of new centrosome proteins by autoimmune patient sera

Compared to other subcellular organelles, centrosome proteome can hardly be studied, due to the dif- ficulties in separation and purification of centrosome. Auto-antisera from 6 autoimmune patients, which recognized centrosome specifically in immunofluorescence, were used to identify the corresponding centrosomal proteins. The sera were first tested by Western blot on whole cell lysate, and all bound antibodies were then eluted from each single band in Western blot membrane to assure which antibody was responsible for the centrosome specific immunofluorescence staining. The corresponding pro- teins were obtained by immunoprecipitation and identified by mass spectrometry. Six centrosomal proteins, including 2 known centrosomal proteins and 4 proteins with unknown localization or report- edly non-centrosomal localization, were identified. These proteins apparently involve in cell cycle regulation, signal transduction pathways, molecular chaperons, and metabolism enzymes, which may reflect the expected functional diversity of centrosome.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Cloning and Expression Pattern of a Gene Encoding a Putative Plastidic ATP/ADP Transporter from Helianthus tuberosus L.

Herein, we report the cloning and molecular characterization of a full cDNA encoding a putative plastidic ATP/ADP transporter, designated HtAATP, for Helianthus tuberosus L. The ATP/ADP translocator protein was isolated from the tuber-cDNA library of H. tuberosus for the first time. The predicted HtAATP protein was judged as a plastidic ATP/ADP translocator protein from its high homology at the amino acid sequence level to the two Arabidopsis thaliana plastidic ATP/ADP translocator proteins AATP1 and AATP2 （84.8% and 79.9% identity, respectively）. Amino acid sequence analysis of the primary structure of HtAATP revealed that it belonged to the plastidic ATP/ADP transporter family. Hydropathy prediction indicated that HtAATP gene product is a highly hydrophobic membrane protein that contains 10 transmembrane domains to form a spanning topology. Southern blotting analysis showed that the HtAATP gene is a single-copy gene in the H. tuberosus genome. Tissue distribution analysis showed that the HtAATP gene is prominently expressed in sink tissues. A stable expression pattern in tubers at different developmental stages implies an active involvement of HtAATP during carbohydrate formation.     

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/.     

Learning cellular sorting pathways using protein interactions and sequence motifs

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/.     

基于序列拓扑和二阶隐马尔可夫模型的跨膜蛋白亚细胞定位预测

现有蛋白质亚细胞定位方法针对水溶性蛋白质而设计,对跨膜蛋白并不适用。而专门的跨膜拓扑预测器,又不是为亚细胞定位而设计的。文章改进了跨膜拓扑预测器TMPHMMLoc的模型结构,设计了一个新的二阶隐马尔可夫模型;采用推广到二阶模型的Baum-Welch算法估计模型参数,并把将各个亚细胞位置建立的模型整合为一个预测器。数据集上测试结果表明,此方法性能显著优于针对可溶性蛋白设计的支持向量机方法和模糊k最邻近方法,也优于TMPHMMLoc中提出的隐马尔可夫模型方法,是一个有效的跨膜蛋白亚细胞定位预测方法。     

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/.     

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.     

Predicting protein subcellular localization by pseudo amino acid composition with a segment-weighted and features-combined approach

Information of protein subcellular location plays an important role in molecular cell biology. Prediction of the subcellular location of proteins will help to understand their functions and interactions. In this paper, a different mode of pseudo amino acid composition was proposed to represent protein samples for predicting their subcellular localization via the following procedures: based on the optimal splice site of each protein sequence, we divided a sequence into sorting signal part and mature protein part, and extracted sequence features from each part separately. Then, the combined features were fed into the SVM classifier to perform the prediction. By the jackknife test on a benchmark dataset in which none of proteins included has more than 90% pairwise sequence identity to any other, the overall accuracies achieved by the method are 94.5% and 90.3% for prokaryotic and eukaryotic proteins, respectively. The results indicate that the prediction quality by our method is quite satisfactory. It is anticipated that the current method may serve as an alternative approach to the existing prediction methods.     

pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes

MOTIVATION: There is a scarcity of efficient computational methods for predicting protein subcellular localization in eukaryotes. Currently available methods are inadequate for genome-scale predictions with several limitations. Here, we present a new prediction method, pTARGET that can predict proteins targeted to nine different subcellular locations in the eukaryotic animal species. RESULTS: The nine subcellular locations predicted by pTARGET include cytoplasm, endoplasmic reticulum, extracellular/secretory, golgi, lysosomes, mitochondria, nucleus, plasma membrane and peroxisomes. Predictions are based on the location-specific protein functional domains and the amino acid compositional differences across different subcellular locations. Overall, this method can predict 68-87% of the true positives at accuracy rates of 96-99%. Comparison of the prediction performance against PSORT showed that pTARGET prediction rates are higher by 11-60% in 6 of the 8 locations tested. Besides, the pTARGET method is robust enough for genome-scale prediction of protein subcellular localizations since, it does not rely on the presence of signal or target peptides. AVAILABILITY: A public web server based on the pTARGET method is accessible at the URL http://bioinformatics.albany.edu/~ptarget. Datasets used for developing pTARGET can be downloaded from this web server. Source code will be available on request from the corresponding author.     

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/.     

Prediction of protein subcellular locations using fuzzy k-NN method

MOTIVATION: Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. RESULTS: In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction is performed with a new data set derived from version 41.0 SWISS-PROT databank, the overall predictive accuracy about 80% has been achieved in a jackknife test. The result demonstrates the applicability of this relative simple method and possible improvement of prediction accuracy for the protein subcellular locations. We also applied this method to annotate six entirely sequenced proteomes, namely Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Oryza sativa, Arabidopsis thaliana and a subset of all human proteins. AVAILABILITY: Supplementary information and subcellular location annotations for eukaryotes are available at http://166.111.30.65/hying/fuzzy_loc.htm