SVM-BetaPred: Prediction of Right-Handed ß-Helix Fold from Protein Sequence Using SVM
Identifieur interne : 000C04 ( Istex/Corpus ); précédent : 000C03; suivant : 000C05SVM-BetaPred: Prediction of Right-Handed ß-Helix Fold from Protein Sequence Using SVM
Auteurs : Siddharth Singh ; Krishnan Hajela ; Ashwini Kumar RamaniSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: The right-handed single-stranded ß-helix proteins are characterized as virulence factors, allergens, toxins that are threat to human health. Identification of these proteins from amino acid sequence is of great importance as these proteins are potential targets for anti-bacterial and fungal agents. In this paper, support vector machine (SVM) has been used to predict the presence of ß-helix fold in protein sequences using dipeptide composition. An input vector of 400 dimensions for dipeptide compositions is used to search for the presence of putative rungs or coils, the conserved secondary structure, found in ß-helix proteins. An average accuracy of 89.2% with Matthew’s correlation coefficient of 0.75 is obtained in a 5-fold cross-validation technique. In addition, a PSSM was also used to score the query sequence of proteins identified as ß-helices by SVM. The method recognizes right-handed ß-helices with 100% sensitivity and 99.6% specificity on test set of known protein structures.
Url:
DOI: 10.1007/978-3-540-75286-8_11
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Identification of these proteins from amino acid sequence is of great importance as these proteins are potential targets for anti-bacterial and fungal agents. In this paper, support vector machine (SVM) has been used to predict the presence of ß-helix fold in protein sequences using dipeptide composition. An input vector of 400 dimensions for dipeptide compositions is used to search for the presence of putative rungs or coils, the conserved secondary structure, found in ß-helix proteins. An average accuracy of 89.2% with Matthew’s correlation coefficient of 0.75 is obtained in a 5-fold cross-validation technique. In addition, a PSSM was also used to score the query sequence of proteins identified as ß-helices by SVM. 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The method recognizes right-handed ß-helices with 100% sensitivity and 99.6% specificity on test set of known protein structures.</p></abstract><textClass ana="keyword"><keywords xml:lang="en"><term>ß-helix fold</term><term>ß-sheet stacking</term><term>pectate lyase</term><term>fold recognition</term><term>secondary structure</term><term>Support Vector Machines</term><term>SVM</term><term>PSSM</term></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I18024</label><item><term type="Secondary" subtype="priority-1">Database Management</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-2">Artificial Intelligence (incl. 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Identification of these proteins from amino acid sequence is of great importance as these proteins are potential targets for anti-bacterial and fungal agents. In this paper, support vector machine (SVM) has been used to predict the presence of ß-helix fold in protein sequences using dipeptide composition. An input vector of 400 dimensions for dipeptide compositions is used to search for the presence of putative rungs or coils, the conserved secondary structure, found in ß-helix proteins. An average accuracy of 89.2% with Matthew’s correlation coefficient of 0.75 is obtained in a 5-fold cross-validation technique. In addition, a PSSM was also used to score the query sequence of proteins identified as ß-helices by SVM. The method recognizes right-handed ß-helices with 100% sensitivity and 99.6% specificity on test set of known protein structures.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>ß-helix fold</Keyword><Keyword>ß-sheet stacking</Keyword><Keyword>pectate lyase</Keyword><Keyword>fold recognition</Keyword><Keyword>secondary structure</Keyword><Keyword>Support Vector Machines</Keyword><Keyword>SVM</Keyword><Keyword>PSSM</Keyword></KeywordGroup></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>SVM-BetaPred: Prediction of Right-Handed ß-Helix Fold from Protein Sequence Using SVM</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>SVM-BetaPred: Prediction of Right-Handed ß-Helix Fold from Protein Sequence Using SVM</title></titleInfo><name type="personal"><namePart type="given">Siddharth</namePart><namePart type="family">Singh</namePart><affiliation>School of Computer Science, </affiliation><affiliation>E-mail: sidssingh@gmail.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Krishnan</namePart><namePart type="family">Hajela</namePart><affiliation>School of Life Science, Devi Ahilya University, Khandwa Road, Indore-452001, India</affiliation><affiliation>E-mail: hajelak@gmail.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ashwini</namePart><namePart type="given">Kumar</namePart><namePart type="family">Ramani</namePart><affiliation>School of Computer Science, </affiliation><affiliation>E-mail: ramani.iips@dauniv.ac.in</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2007</dateIssued><copyrightDate encoding="w3cdtf">2007</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: The right-handed single-stranded ß-helix proteins are characterized as virulence factors, allergens, toxins that are threat to human health. Identification of these proteins from amino acid sequence is of great importance as these proteins are potential targets for anti-bacterial and fungal agents. In this paper, support vector machine (SVM) has been used to predict the presence of ß-helix fold in protein sequences using dipeptide composition. An input vector of 400 dimensions for dipeptide compositions is used to search for the presence of putative rungs or coils, the conserved secondary structure, found in ß-helix proteins. An average accuracy of 89.2% with Matthew’s correlation coefficient of 0.75 is obtained in a 5-fold cross-validation technique. In addition, a PSSM was also used to score the query sequence of proteins identified as ß-helices by SVM. The method recognizes right-handed ß-helices with 100% sensitivity and 99.6% specificity on test set of known protein structures.</abstract><subject lang="en"><genre>Keywords</genre><topic>ß-helix fold</topic><topic>ß-sheet stacking</topic><topic>pectate lyase</topic><topic>fold recognition</topic><topic>secondary structure</topic><topic>Support Vector Machines</topic><topic>SVM</topic><topic>PSSM</topic></subject><relatedItem type="host"><titleInfo><title>Pattern Recognition in Bioinformatics</title><subTitle>Second IAPR International Workshop, PRIB 2007, Singapore, October 1-2, 2007. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Jagath</namePart><namePart type="given">C.</namePart><namePart type="family">Rajapakse</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bertil</namePart><namePart type="family">Schmidt</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gwenn</namePart><namePart type="family">Volkert</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2007</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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