Prediction of functional class of the SARS coronavirus proteins by a statistical learning method.
Identifieur interne : 001190 ( Ncbi/Merge ); précédent : 001189; suivant : 001191Prediction of functional class of the SARS coronavirus proteins by a statistical learning method.
Auteurs : C Z Cai [Singapour] ; L Y Han ; X. Chen ; Z W Cao ; Y Z ChenSource :
- Journal of proteome research [ 1535-3893 ]
Descripteurs français
- KwdFr :
- Adénosine triphosphate (), Algorithmes, Alignement de séquences, Analyse de séquence de protéine, Bases de données de protéines, Biologie informatique (), Cadres ouverts de lecture, Génome viral, Intelligence artificielle, Logiciel, Modèles statistiques, Protéines virales (), Protéome, Protéomique (), Virus du SRAS ().
- MESH :
- Adénosine triphosphate, Algorithmes, Alignement de séquences, Analyse de séquence de protéine, Bases de données de protéines, Biologie informatique, Cadres ouverts de lecture, Génome viral, Intelligence artificielle, Logiciel, Modèles statistiques, Protéines virales, Protéome, Protéomique, Virus du SRAS.
English descriptors
- KwdEn :
- Adenosine Triphosphate (chemistry), Algorithms, Artificial Intelligence, Computational Biology (methods), Databases, Protein, Genome, Viral, Models, Statistical, Open Reading Frames, Proteome, Proteomics (methods), SARS Virus (chemistry), Sequence Alignment, Sequence Analysis, Protein, Software, Viral Proteins (chemistry).
- MESH :
- chemical , chemistry : Adenosine Triphosphate, Viral Proteins.
- chemistry : SARS Virus.
- methods : Computational Biology, Proteomics.
- Algorithms, Artificial Intelligence, Databases, Protein, Genome, Viral, Models, Statistical, Open Reading Frames, Proteome, Sequence Alignment, Sequence Analysis, Protein, Software.
Abstract
The complete genome of severe acute respiratory syndrome coronavirus (SARS-CoV) reveals the existence of putative proteins unique to SARS-CoV. Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .
DOI: 10.1021/pr050110a
PubMed: 16212442
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Chen</name></author><author><name sortKey="Cao, Z W" sort="Cao, Z W" uniqKey="Cao Z" first="Z W" last="Cao">Z W Cao</name></author><author><name sortKey="Chen, Y Z" sort="Chen, Y Z" uniqKey="Chen Y" first="Y Z" last="Chen">Y Z Chen</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="????"><PubDate><MedlineDate>2005 Sep-Oct</MedlineDate></PubDate></date><idno type="RBID">pubmed:16212442</idno><idno type="pmid">16212442</idno><idno type="doi">10.1021/pr050110a</idno><idno type="wicri:Area/PubMed/Corpus">002514</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">002514</idno><idno type="wicri:Area/PubMed/Curation">002514</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">002514</idno><idno type="wicri:Area/PubMed/Checkpoint">003521</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">003521</idno><idno type="wicri:Area/Ncbi/Merge">001190</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Prediction of functional class of the SARS coronavirus proteins by a statistical learning method.</title><author><name sortKey="Cai, C Z" sort="Cai, C Z" uniqKey="Cai C" first="C Z" last="Cai">C Z Cai</name><affiliation wicri:level="4"><nlm:affiliation>Bioinformatics and Drug Design Group, Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543.</nlm:affiliation><orgName type="university">Université nationale de Singapour</orgName><country>Singapour</country></affiliation></author><author><name sortKey="Han, L Y" sort="Han, L Y" uniqKey="Han L" first="L Y" last="Han">L Y Han</name></author><author><name sortKey="Chen, X" sort="Chen, X" uniqKey="Chen X" first="X" last="Chen">X. 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Identification of their function facilitates a mechanistic understanding of SARS infection and drug development for its treatment. The sequence of the majority of these putative proteins has no significant similarity to those of known proteins, which complicates the task of using sequence analysis tools to probe their function. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to SARS-CoV proteins. Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi .</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">16212442</PMID><DateCompleted><Year>2006</Year><Month>01</Month><Day>03</Day></DateCompleted><DateRevised><Year>2013</Year><Month>11</Month><Day>21</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">1535-3893</ISSN><JournalIssue CitedMedium="Print"><Volume>4</Volume><Issue>5</Issue><PubDate><MedlineDate>2005 Sep-Oct</MedlineDate></PubDate></JournalIssue><Title>Journal of proteome research</Title><ISOAbbreviation>J. 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Testing results indicate that SVM is able to predict the functional class of 73% of the known SARS-CoV proteins with available sequences and 67% of 18 other novel viral proteins. A combination of the sequence comparison method BLAST and SVMProt can further improve the prediction accuracy of SMVProt such that the functional class of two additional SARS-CoV proteins is correctly predicted. Our study suggests that the SARS-CoV genome possibly contains a putative voltage-gated ion channel, structural proteins, a carbon-oxygen lyase, oxidoreductases acting on the CH-OH group of donors, and an ATP-binding cassette transporter. 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Chen</name><name sortKey="Chen, Y Z" sort="Chen, Y Z" uniqKey="Chen Y" first="Y Z" last="Chen">Y Z Chen</name><name sortKey="Han, L Y" sort="Han, L Y" uniqKey="Han L" first="L Y" last="Han">L Y Han</name></noCountry><country name="Singapour"><noRegion><name sortKey="Cai, C Z" sort="Cai, C Z" uniqKey="Cai C" first="C Z" last="Cai">C Z Cai</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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