SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction
Identifieur interne : 000A95 ( Pmc/Curation ); précédent : 000A94; suivant : 000A96SVM-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.
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>
<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
</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>
<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>
<affiliation><nlm:aff id="I1">Singapore Immunology Network, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648</nlm:aff>
<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
</affiliation>
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<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
</affiliation>
<affiliation><nlm:aff id="I1">Singapore Immunology Network, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648</nlm:aff>
<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
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<affiliation wicri:level="4"><nlm:aff id="I3">Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597</nlm:aff>
<orgName type="university">Université nationale de Singapour</orgName>
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<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>
<affiliation wicri:level="4"><nlm:aff id="I3">Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597</nlm:aff>
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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>
<author><name sortKey="Wee, Lawrence Jk" sort="Wee, Lawrence Jk" uniqKey="Wee L" first="Lawrence Jk" last="Wee">Lawrence Jk Wee</name>
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<author><name sortKey="Simarmata, Diane" sort="Simarmata, Diane" uniqKey="Simarmata D" first="Diane" last="Simarmata">Diane Simarmata</name>
<affiliation><nlm:aff id="I1">Singapore Immunology Network, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648</nlm:aff>
<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
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<author><name sortKey="Kam, Yiu Wing" sort="Kam, Yiu Wing" uniqKey="Kam Y" first="Yiu-Wing" last="Kam">Yiu-Wing Kam</name>
<affiliation><nlm:aff id="I1">Singapore Immunology Network, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648</nlm:aff>
<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
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<author><name sortKey="Ng, Lisa Fp" sort="Ng, Lisa Fp" uniqKey="Ng L" first="Lisa Fp" last="Ng">Lisa Fp Ng</name>
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<wicri:noCountry code="subfield">Singapore 138648</wicri:noCountry>
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<country>Singapour</country>
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<author><name sortKey="Tong, Joo Chuan" sort="Tong, Joo Chuan" uniqKey="Tong J" first="Joo Chuan" last="Tong">Joo Chuan Tong</name>
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<wicri:noCountry code="subfield">Singapore 138632</wicri:noCountry>
</affiliation>
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<series><title level="j">BMC Genomics</title>
<idno type="eISSN">1471-2164</idno>
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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>
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<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Genomics</journal-id>
<journal-title-group><journal-title>BMC Genomics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2164</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">21143805</article-id>
<article-id pub-id-type="pmc">3005920</article-id>
<article-id pub-id-type="publisher-id">1471-2164-11-S4-S21</article-id>
<article-id pub-id-type="doi">10.1186/1471-2164-11-S4-S21</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Proceedings</subject>
</subj-group>
</article-categories>
<title-group><article-title>SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" id="A1"><name><surname>Wee</surname>
<given-names>Lawrence JK</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<xref ref-type="aff" rid="I2">2</xref>
<email>lawrence@bic.nus.edu.sg</email>
</contrib>
<contrib contrib-type="author" id="A2"><name><surname>Simarmata</surname>
<given-names>Diane</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>diane_simarmata@immunol.a-star.edu.sg</email>
</contrib>
<contrib contrib-type="author" id="A3"><name><surname>Kam</surname>
<given-names>Yiu-Wing</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>Jason_Kam@immunol.a-star.edu.sg</email>
</contrib>
<contrib contrib-type="author" id="A4"><name><surname>Ng</surname>
<given-names>Lisa FP</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<xref ref-type="aff" rid="I3">3</xref>
<email>lisa_ng@immunol.a-star.edu.sg</email>
</contrib>
<contrib contrib-type="author" corresp="yes" id="A5"><name><surname>Tong</surname>
<given-names>Joo Chuan</given-names>
</name>
<xref ref-type="aff" rid="I2">2</xref>
<xref ref-type="aff" rid="I3">3</xref>
<email>victor@bic.nus.edu.sg</email>
</contrib>
</contrib-group>
<aff id="I1"><label>1</label>
Singapore Immunology Network, 8A Biomedical Grove, #04-06 Immunos, Biopolis, Singapore 138648</aff>
<aff id="I2"><label>2</label>
Data Mining Department, Institute for Infocomm Research, 1 Fusionopolis Way, #21-01 Connexis South Tower, Singapore 138632</aff>
<aff id="I3"><label>3</label>
Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597</aff>
<pub-date pub-type="collection"><year>2010</year>
</pub-date>
<pub-date pub-type="epub"><day>2</day>
<month>12</month>
<year>2010</year>
</pub-date>
<volume>11</volume>
<issue>Suppl 4</issue>
<supplement><named-content content-type="supplement-title">Ninth International Conference on Bioinformatics (InCoB2010): Computational Biology</named-content>
<named-content content-type="supplement-editor">Christian Schönbach, Kenta Nakai, Tin Wee Tan and Shoba Ranganathan</named-content>
<ext-link ext-link-type="uri" xlink:href="http://www.biomedcentral.com/content/pdf/1471-2164-11-S4-info.pdf">http://www.biomedcentral.com/content/pdf/1471-2164-11-S4-info.pdf</ext-link>
</supplement>
<fpage>S21</fpage>
<lpage>S21</lpage>
<permissions><copyright-statement>Copyright ©2010 Wee et al; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2010</copyright-year>
<copyright-holder>Wee et al; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.biomedcentral.com/1471-2164/11/S4/S21"></self-uri>
<abstract><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>
</abstract>
<conference><conf-date>26–28 September 2010</conf-date>
<conf-name>Asia Pacific Bioinformatics Network (APBioNet) Ninth International Conference on Bioinformatics (InCoB2010)</conf-name>
<conf-loc>Tokyo, Japan</conf-loc>
</conference>
</article-meta>
</front>
</pmc>
</record>
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