Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components
The proteins structure can be mainly classified into four classes: all‐α, all‐β, α/β, and α + β protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discri...
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Veröffentlicht in: | Journal of computational chemistry 2007-07, Vol.28 (9), p.1463-1466 |
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description | The proteins structure can be mainly classified into four classes: all‐α, all‐β, α/β, and α + β protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007 |
doi_str_mv | 10.1002/jcc.20554 |
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For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007</description><identifier>ISSN: 0192-8651</identifier><identifier>EISSN: 1096-987X</identifier><identifier>DOI: 10.1002/jcc.20554</identifier><identifier>PMID: 17330882</identifier><identifier>CODEN: JCCHDD</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Algorithms ; Amino acids ; Amino Acids - chemistry ; Biochemistry ; Databases, Protein ; dipeptide correlation ; Dipeptides - chemistry ; increment of diversity ; Models, Biological ; Peptides ; Protein Conformation ; protein structural class ; Proteins ; Proteins - chemistry ; Proteins - classification ; quadratic discriminant</subject><ispartof>Journal of computational chemistry, 2007-07, Vol.28 (9), p.1463-1466</ispartof><rights>Copyright © 2007 Wiley Periodicals, Inc.</rights><rights>Copyright (c) 2007 Wiley Periodicals, Inc.</rights><rights>Copyright John Wiley and Sons, Limited Jul 15, 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4544-1822056078393926c143b6879cce096f331a24b656059e305965066e28fc1c753</citedby><cites>FETCH-LOGICAL-c4544-1822056078393926c143b6879cce096f331a24b656059e305965066e28fc1c753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjcc.20554$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjcc.20554$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17330882$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Hao</creatorcontrib><creatorcontrib>Li, Qian-Zhong</creatorcontrib><title>Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components</title><title>Journal of computational chemistry</title><addtitle>J. Comput. Chem</addtitle><description>The proteins structure can be mainly classified into four classes: all‐α, all‐β, α/β, and α + β protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007</description><subject>Algorithms</subject><subject>Amino acids</subject><subject>Amino Acids - chemistry</subject><subject>Biochemistry</subject><subject>Databases, Protein</subject><subject>dipeptide correlation</subject><subject>Dipeptides - chemistry</subject><subject>increment of diversity</subject><subject>Models, Biological</subject><subject>Peptides</subject><subject>Protein Conformation</subject><subject>protein structural class</subject><subject>Proteins</subject><subject>Proteins - chemistry</subject><subject>Proteins - classification</subject><subject>quadratic discriminant</subject><issn>0192-8651</issn><issn>1096-987X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kU1v1DAQhi0EokvhwB9AFgckDmn9FcfhVq1gW1rBAapys7zOLHhJ7GA7gj3w3_E22yIh4cPMwc88mtGL0HNKTigh7HRr7QkjdS0eoAUlraxa1Xx5iBaEtqxSsqZH6ElKW0IIr6V4jI5owzlRii3Q7-vk_Fc8Jpi6gM3gfKnWddiGYQzJZRc8zgGPETpnc-khg_M45TjZPEXTY9ublN7gs7H8GfsNOrzeYedtiGOIJu_1ghDcuRHG7DqY1R58Tk_Ro43pEzw79GN0_e7t5-V5dfVxdbE8u6qsqIWoqGLlPEkaxVveMmmp4GupmtZaKOduOKeGibUsSN0CL0XWREpgamOpbWp-jF7N3rLijwlS1oNLFvreeAhT0g0RglFBCvjyH3AbpujLbprtnxCcF-j1DNkYUoqw0WN0g4k7TYneB6JLIPo2kMK-OAin9QDdX_KQQAFOZ-Cn62H3f5N-v1zeKat5wqUMv-4nTPyuZcObWt98WOkbdckuV5-UPud_AJ9poyo</recordid><startdate>20070715</startdate><enddate>20070715</enddate><creator>Lin, Hao</creator><creator>Li, Qian-Zhong</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>7X8</scope></search><sort><creationdate>20070715</creationdate><title>Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components</title><author>Lin, Hao ; Li, Qian-Zhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4544-1822056078393926c143b6879cce096f331a24b656059e305965066e28fc1c753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Amino acids</topic><topic>Amino Acids - chemistry</topic><topic>Biochemistry</topic><topic>Databases, Protein</topic><topic>dipeptide correlation</topic><topic>Dipeptides - chemistry</topic><topic>increment of diversity</topic><topic>Models, Biological</topic><topic>Peptides</topic><topic>Protein Conformation</topic><topic>protein structural class</topic><topic>Proteins</topic><topic>Proteins - chemistry</topic><topic>Proteins - classification</topic><topic>quadratic discriminant</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Hao</creatorcontrib><creatorcontrib>Li, Qian-Zhong</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of computational chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Hao</au><au>Li, Qian-Zhong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components</atitle><jtitle>Journal of computational chemistry</jtitle><addtitle>J. Comput. Chem</addtitle><date>2007-07-15</date><risdate>2007</risdate><volume>28</volume><issue>9</issue><spage>1463</spage><epage>1466</epage><pages>1463-1466</pages><issn>0192-8651</issn><eissn>1096-987X</eissn><coden>JCCHDD</coden><abstract>The proteins structure can be mainly classified into four classes: all‐α, all‐β, α/β, and α + β protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with Quadratic Discriminant analysis, is presented to study and predict protein structural class. On the basis of the concept of the pseudo amino acid composition (Chou, Proteins: Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60), 400 dipeptide components and 20 amino acid composition are, respectively, selected as parameters of diversity source. Total of 204 nonhomologous proteins constructed by Chou (Chou, Biochem Biophys Res Commun 1999, 264, 216) are used for training and testing the predictive model. The predicted results by using the pseudo amino acids approach as proposed in this paper can remarkably improve the success rates, and hence the current method may play a complementary role to other existing methods for predicting protein structural classification. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><pmid>17330882</pmid><doi>10.1002/jcc.20554</doi><tpages>4</tpages></addata></record> |
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subjects | Algorithms Amino acids Amino Acids - chemistry Biochemistry Databases, Protein dipeptide correlation Dipeptides - chemistry increment of diversity Models, Biological Peptides Protein Conformation protein structural class Proteins Proteins - chemistry Proteins - classification quadratic discriminant |
title | Using pseudo amino acid composition to predict protein structural class: Approached by incorporating 400 dipeptide components |
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