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SSPred: A prediction server based on SVM for the identification and classification of proteins involved in bacterial secretion systems.

ABSTRACT: Protein secretion systems used by almost all bacteria are highly significant for the normal existence and interaction of bacteria with their host. The accumulation of genome sequence data in past few years has provided great insights into the distribution and function of these secretion systems. In this study, a support vector machine (SVM)- based method, SSPred was developed for the automated functional annotation of proteins involved in secretion systems further classifying them into five major sub-types (Type-I, Type-II, Type-III, Type-IV and Sec systems). The dataset used in this study for training and testing was obtained from KEGG and SwissProt database and was curated in order to avoid redundancy. To overcome the problem of imbalance in positive and negative dataset, an ensemble of SVM modules, each trained on a balanced subset of the training data were used. Firstly, protein sequence features like amino-acid composition (AAC), dipeptide composition (DPC) and physico-chemical composition (PCC) were used to develop the SVM-based modules that achieved an average accuracy of 84%, 85.17% and 82.59%, respectively. Secondly, a hybrid module (hybrid-I) integrating all the previously used features was developed that achieved an average accuracy of 86.12%. Another hybrid module (hybrid-II) developed using evolutionary information of a protein sequence extracted from position-specific scoring matrix and amino-acid composition achieved a maximum average accuracy of 89.73%. On unbiased evaluation using an independent data set, SSPred showed good prediction performance in identification and classification of secretion systems. SSPred is a freely available World Wide Web server at http//www.bioinformatics.org/sspred.

SUBMITTER: Pundhir S

PROVIDER: S-EPMC3163916 | biostudies-other | 2011

REPOSITORIES: biostudies-other

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SSPred: A prediction server based on SVM for the identification and classification of proteins involved in bacterial secretion systems.

Pundhir Sachin S Kumar Anil A

Bioinformation 20110802 10

Protein secretion systems used by almost all bacteria are highly significant for the normal existence and interaction of bacteria with their host. The accumulation of genome sequence data in past few years has provided great insights into the distribution and function of these secretion systems. In this study, a support vector machine (SVM)- based method, SSPred was developed for the automated functional annotation of proteins involved in secretion systems further classifying them into five majo ...[more]

PMID: 21904425

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Project description:BackgroundPrediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens.ResultsIn the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features.ConclusionVirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server - VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.

Project description:BackgroundThe nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.ResultsAll modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM) based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC) of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC) and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM) based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins http://www.imtech.res.in/raghava/nppred/.ConclusionThis study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.

Dataset Information

SSPred: A prediction server based on SVM for the identification and classification of proteins involved in bacterial secretion systems.

Publications

SSPred: A prediction server based on SVM for the identification and classification of proteins involved in bacterial secretion systems.

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