10.1186/1471-2164-11-S4-S21BMC Genomics11SUPPL. 4S2

Kam, Yiu-Wing

Ng, Lisa FP

Simarmata, Diane

Tong, Joo Chuan

Wee, Lawrence JK

BMC Genomics

English

PubMed

Lawrence JK Wee

Diane Simarmata

Yiu-Wing Kam

Lisa FP Ng

Joo Chuan Tong

Springer - Publisher Connector

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

Wee, L.J.K

Simarmata, D

Kam, Y.-W

Ng, L.F.P

Tong, J.C

ScholarBank@NUS

Abstract
          
            Backgound
            The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.
          
          
            Results
            We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes.
          
          
            Conclusion
            We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.
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Crossref

A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery

Flower DR: Benchmarking B cell epitope prediction underperformance of existing methods. Protein Sci

Honavar V: Predicting linear B-cell epitopes using string kernels. J Mol Recognit

Honavar V: Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information.

Improved method for predicting linear B-cell epitopes. Immunome Res

KC: Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids

Lin CJ: LIBSVM: a library for support vector machines.

Machine learning approaches for prediction of linear B-cell epitopes on proteins. J Mol Recognit

Ngai SM: Computational identification of protein methylation sites through bi-profile Bayes feature extraction. PLoS One

P: COBEpro: a novel system for predicting continuous B-cell epitopes. Protein Eng Des Sel

Pellequer JL: BEPITOPE: predicting the location of continuous epitopes and patterns in proteins.

Predicting protein-protein interactions based only on sequences information.

Predicting residue-wise contact orders in proteins by support vector regression.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine

Pupko T: A machine-learning approach for predicting B-cell epitopes. Mol Immunol

Pupko T: Epitopia: a web-server for predicting B-cell epitopes.

Raghava GP: Bcipep: a database of B-cell epitopes.

Raghava GP: Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins

Raghava GPS: BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties.

The UniProt Consortium: The Universal Protein Resource (Uniprot). Nucleic Acids Res

Westhof E: PREDITOP: a program for antigenicity prediction.

Whisstock JC: Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics

Yusim K: Immunoinformatics comes of age.

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3005920

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

Abstract

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Springer - Publisher Connector

Springer - Publisher Connector

ScholarBank@NUS

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