SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
Identifieur interne : 000792 ( Ncbi/Curation ); précédent : 000791; suivant : 000793SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
Auteurs : Lawrence Jk Wee ; Diane Simarmata ; Yiu-Wing Kam ; Lisa Fp Ng [Singapour] ; Joo Chuan Tong [Singapour]Source :
- BMC Genomics [ 1471-2164 ] ; 2010.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Antigens, Peptides.
- chemical , immunology : Antigens, Epitopes, B-Lymphocyte, Peptides.
- Algorithms, Bayes Theorem, Benchmarking, Computer Simulation, Internet, Predictive Value of Tests.
Abstract
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,
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
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
Url:
DOI: 10.1186/1471-2164-11-S4-S21
PubMed: 21143805
PubMed Central: 3005920
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Lawrence Jk Wee<affiliation><nlm:aff id="I1">Singapore Immunology Network, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648</nlm:aff>
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</affiliation>
<affiliation><nlm:aff id="I2">Data Mining Department, Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis South Tower, Singapore 138632</nlm:aff>
<wicri:noCountry code="subfield">Singapore 138632</wicri:noCountry>
</affiliation>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction</title>
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<series><title level="j">BMC Genomics</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Antigens (chemistry)</term>
<term>Antigens (immunology)</term>
<term>Bayes Theorem</term>
<term>Benchmarking</term>
<term>Computer Simulation</term>
<term>Epitopes, B-Lymphocyte (immunology)</term>
<term>Internet</term>
<term>Peptides (chemistry)</term>
<term>Peptides (immunology)</term>
<term>Predictive Value of Tests</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Antigènes ()</term>
<term>Antigènes (immunologie)</term>
<term>Déterminants antigéniques des lymphocytes B (immunologie)</term>
<term>Internet</term>
<term>Peptides ()</term>
<term>Peptides (immunologie)</term>
<term>Référenciation</term>
<term>Simulation numérique</term>
<term>Théorème de Bayes</term>
<term>Valeur prédictive des tests</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Antigens</term>
<term>Peptides</term>
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<term>Epitopes, B-Lymphocyte</term>
<term>Peptides</term>
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<term>Déterminants antigéniques des lymphocytes B</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Backgound</title>
<p>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, <italic>in silico</italic>
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.</p>
</sec>
<sec><title>Results</title>
<p>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 <italic>in silico</italic>
. 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 A<sub>ROC</sub>
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.</p>
</sec>
<sec><title>Conclusion</title>
<p>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 <ext-link ext-link-type="uri" xlink:href="http://www.immunopred.org/bayesb/index.html">http://www.immunopred.org/bayesb/index.html</ext-link>
.</p>
</sec>
</div>
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