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Identifying RNA-binding residues based on evolutionary conserved structural and energetic features
Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because fun...
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Published in: | Nucleic acids research 2014-02, Vol.42 (3), p.e15-e15 |
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creator | Chen, Yao Chi Sargsyan, Karen Wright, Jon D Huang, Yi-Shuian Lim, Carmay |
description | Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to 'novel' protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the 'unknown' RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw. |
doi_str_mv | 10.1093/nar/gkt1299 |
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Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to 'novel' protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the 'unknown' RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw.</description><identifier>ISSN: 0305-1048</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkt1299</identifier><identifier>PMID: 24343026</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Amino Acids - chemistry ; Binding Sites ; DNA-Binding Proteins - chemistry ; Evolution, Molecular ; Humans ; Methods Online ; Protein Binding ; Protein Conformation ; RNA - chemistry ; RNA - metabolism ; RNA-Binding Proteins - chemistry ; RNA-Binding Proteins - metabolism ; Software ; Static Electricity</subject><ispartof>Nucleic acids research, 2014-02, Vol.42 (3), p.e15-e15</ispartof><rights>The Author(s) 2013. 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Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to 'novel' protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the 'unknown' RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw.</description><subject>Amino Acids - chemistry</subject><subject>Binding Sites</subject><subject>DNA-Binding Proteins - chemistry</subject><subject>Evolution, Molecular</subject><subject>Humans</subject><subject>Methods Online</subject><subject>Protein Binding</subject><subject>Protein Conformation</subject><subject>RNA - chemistry</subject><subject>RNA - metabolism</subject><subject>RNA-Binding Proteins - chemistry</subject><subject>RNA-Binding Proteins - metabolism</subject><subject>Software</subject><subject>Static Electricity</subject><issn>0305-1048</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNpVkFtLAzEQhYMotlaffJf8gbW57-ZFKMVLQRREn5dsMrtG22xJdgv990a8oE8zzGHOfGcQOqfkkhLN58HEefc-UKb1AZpSrlghtGKHaEo4kQUlopqgk5TeCKGCSnGMJkxwwQlTU9SsHITBt3sfOvz0sCgaH9xnHyF5N0LCjUngcB8w7Pr1OPg-39tj24cEcZeVNMTRDmM0a2yCwxAgdjB4i1sweQzpFB21Zp3g7LvO0MvN9fPyrrh_vF0tF_fFlio-FExI0tiqFaIFrjJ5S5i11JpSUUkrVToCHBiRpamIbilQoU3FpWwsEUqUfIauvny3Y7MBZ3OuDFVvo99k4ro3vv6vBP9ad_2u5ppqWbFscPHX4Hfz51v8AyZzbwI</recordid><startdate>20140201</startdate><enddate>20140201</enddate><creator>Chen, Yao Chi</creator><creator>Sargsyan, Karen</creator><creator>Wright, Jon D</creator><creator>Huang, Yi-Shuian</creator><creator>Lim, Carmay</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>5PM</scope></search><sort><creationdate>20140201</creationdate><title>Identifying RNA-binding residues based on evolutionary conserved structural and energetic features</title><author>Chen, Yao Chi ; Sargsyan, Karen ; Wright, Jon D ; Huang, Yi-Shuian ; Lim, Carmay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p163t-2450bc8f44fe36305f02cc1ca76151867d0e3e2057a809f1e149a8355bc046473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Amino Acids - chemistry</topic><topic>Binding Sites</topic><topic>DNA-Binding Proteins - chemistry</topic><topic>Evolution, Molecular</topic><topic>Humans</topic><topic>Methods Online</topic><topic>Protein Binding</topic><topic>Protein Conformation</topic><topic>RNA - chemistry</topic><topic>RNA - metabolism</topic><topic>RNA-Binding Proteins - chemistry</topic><topic>RNA-Binding Proteins - metabolism</topic><topic>Software</topic><topic>Static Electricity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yao Chi</creatorcontrib><creatorcontrib>Sargsyan, Karen</creatorcontrib><creatorcontrib>Wright, Jon D</creatorcontrib><creatorcontrib>Huang, Yi-Shuian</creatorcontrib><creatorcontrib>Lim, Carmay</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nucleic acids research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Yao Chi</au><au>Sargsyan, Karen</au><au>Wright, Jon D</au><au>Huang, Yi-Shuian</au><au>Lim, Carmay</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifying RNA-binding residues based on evolutionary conserved structural and energetic features</atitle><jtitle>Nucleic acids research</jtitle><addtitle>Nucleic Acids Res</addtitle><date>2014-02-01</date><risdate>2014</risdate><volume>42</volume><issue>3</issue><spage>e15</spage><epage>e15</epage><pages>e15-e15</pages><issn>0305-1048</issn><eissn>1362-4962</eissn><abstract>Increasing numbers of protein structures are solved each year, but many of these structures belong to proteins whose sequences are homologous to sequences in the Protein Data Bank. Nevertheless, the structures of homologous proteins belonging to the same family contain useful information because functionally important residues are expected to preserve physico-chemical, structural and energetic features. This information forms the basis of our method, which detects RNA-binding residues of a given RNA-binding protein as those residues that preserve physico-chemical, structural and energetic features in its homologs. Tests on 81 RNA-bound and 35 RNA-free protein structures showed that our method yields a higher fraction of true RNA-binding residues (higher precision) than two structure-based and two sequence-based machine-learning methods. Because the method requires no training data set and has no parameters, its precision does not degrade when applied to 'novel' protein sequences unlike methods that are parameterized for a given training data set. It was used to predict the 'unknown' RNA-binding residues in the C-terminal RNA-binding domain of human CPEB3. The two predicted residues, F430 and F474, were experimentally verified to bind RNA, in particular F430, whose mutation to alanine or asparagine nearly abolished RNA binding. The method has been implemented in a webserver called DR_bind1, which is freely available with no login requirement at http://drbind.limlab.ibms.sinica.edu.tw.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>24343026</pmid><doi>10.1093/nar/gkt1299</doi><oa>free_for_read</oa></addata></record> |
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subjects | Amino Acids - chemistry Binding Sites DNA-Binding Proteins - chemistry Evolution, Molecular Humans Methods Online Protein Binding Protein Conformation RNA - chemistry RNA - metabolism RNA-Binding Proteins - chemistry RNA-Binding Proteins - metabolism Software Static Electricity |
title | Identifying RNA-binding residues based on evolutionary conserved structural and energetic features |
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