iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features
Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly‐SS, a protein lysine glycation site identification method ba...
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Veröffentlicht in: | Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2018-07, Vol.86 (7), p.777-789 |
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creator | Islam, Md Mofijul Saha, Sanjay Rahman, Md Mahmudur Shatabda, Swakkhar Farid, Dewan Md Dehzangi, Abdollah |
description | Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly‐SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly‐SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/. |
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This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly‐SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly‐SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.</description><identifier>ISSN: 0887-3585</identifier><identifier>EISSN: 1097-0134</identifier><identifier>DOI: 10.1002/prot.25511</identifier><identifier>PMID: 29675975</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Amino acid composition ; Amino acid sequence ; Chemical bonds ; Chemical reactions ; classification ; evolutionary features ; Feature extraction ; feature selection ; Glycosylation ; Internet ; Lysine ; Molecular chains ; Organic chemistry ; Polarity ; protein glycation ; Protein structure ; Proteins ; Secondary structure ; Servers ; structural features ; Sugar ; Support vector machines</subject><ispartof>Proteins, structure, function, and bioinformatics, 2018-07, Vol.86 (7), p.777-789</ispartof><rights>2018 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4231-6e2937285e65ba0e343c4f85b95c6c3eb9975bf39783f1cc197489b56e6eaafa3</citedby><cites>FETCH-LOGICAL-c4231-6e2937285e65ba0e343c4f85b95c6c3eb9975bf39783f1cc197489b56e6eaafa3</cites><orcidid>0000-0003-0669-072X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fprot.25511$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fprot.25511$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27926,27927,45576,45577</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29675975$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Islam, Md Mofijul</creatorcontrib><creatorcontrib>Saha, Sanjay</creatorcontrib><creatorcontrib>Rahman, Md Mahmudur</creatorcontrib><creatorcontrib>Shatabda, Swakkhar</creatorcontrib><creatorcontrib>Farid, Dewan Md</creatorcontrib><creatorcontrib>Dehzangi, Abdollah</creatorcontrib><title>iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features</title><title>Proteins, structure, function, and bioinformatics</title><addtitle>Proteins</addtitle><description>Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly‐SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly‐SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.</description><subject>Amino acid composition</subject><subject>Amino acid sequence</subject><subject>Chemical bonds</subject><subject>Chemical reactions</subject><subject>classification</subject><subject>evolutionary features</subject><subject>Feature extraction</subject><subject>feature selection</subject><subject>Glycosylation</subject><subject>Internet</subject><subject>Lysine</subject><subject>Molecular chains</subject><subject>Organic chemistry</subject><subject>Polarity</subject><subject>protein glycation</subject><subject>Protein structure</subject><subject>Proteins</subject><subject>Secondary structure</subject><subject>Servers</subject><subject>structural features</subject><subject>Sugar</subject><subject>Support vector machines</subject><issn>0887-3585</issn><issn>1097-0134</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKxDAYRoMoOo5ufAAJuBGhYy7NzZ2INxAUHbeWNPN3iHRabVqkOx_BZ_RJTJ3RhQtXIeTk8HEQ2qNkQglhxy9N3U6YEJSuoRElRiWE8nQdjYjWKuFCiy20HcIzIUQaLjfRFjNSCaPECD35u_j7suw_3z8eHk7w9Qyq1he9r-Z48IKv8LzsnW19XeHgWwi4C8NrgNcOKgfYVjMc2qZzbdcAzm2AGS7ADrewgzYKWwbYXZ1j9HhxPj27Sm5uL6_PTm8SlzJOEwnMcMW0AClyS4Cn3KWFFrkRTjoOuYlj84IbpXlBnaNGpdrkQoIEawvLx-hw6Y2b46zQZgsfHJSlraDuQsYI00ZyzXRED_6gz3XXVHFdpISiSquYb4yOlpRr6hAaKLKXxi9s02eUZEP1bKiTfVeP8P5K2eULmP2iP5kjQJfAmy-h_0eV3d3fTpfSL5PAjpI</recordid><startdate>201807</startdate><enddate>201807</enddate><creator>Islam, Md Mofijul</creator><creator>Saha, Sanjay</creator><creator>Rahman, Md Mahmudur</creator><creator>Shatabda, Swakkhar</creator><creator>Farid, Dewan Md</creator><creator>Dehzangi, Abdollah</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QO</scope><scope>7QP</scope><scope>7QR</scope><scope>7TK</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0669-072X</orcidid></search><sort><creationdate>201807</creationdate><title>iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features</title><author>Islam, Md Mofijul ; Saha, Sanjay ; Rahman, Md Mahmudur ; Shatabda, Swakkhar ; Farid, Dewan Md ; Dehzangi, Abdollah</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4231-6e2937285e65ba0e343c4f85b95c6c3eb9975bf39783f1cc197489b56e6eaafa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Amino acid composition</topic><topic>Amino acid sequence</topic><topic>Chemical bonds</topic><topic>Chemical reactions</topic><topic>classification</topic><topic>evolutionary features</topic><topic>Feature extraction</topic><topic>feature selection</topic><topic>Glycosylation</topic><topic>Internet</topic><topic>Lysine</topic><topic>Molecular chains</topic><topic>Organic chemistry</topic><topic>Polarity</topic><topic>protein glycation</topic><topic>Protein structure</topic><topic>Proteins</topic><topic>Secondary structure</topic><topic>Servers</topic><topic>structural features</topic><topic>Sugar</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Islam, Md Mofijul</creatorcontrib><creatorcontrib>Saha, Sanjay</creatorcontrib><creatorcontrib>Rahman, Md Mahmudur</creatorcontrib><creatorcontrib>Shatabda, Swakkhar</creatorcontrib><creatorcontrib>Farid, Dewan Md</creatorcontrib><creatorcontrib>Dehzangi, Abdollah</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Proteins, structure, function, and bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Islam, Md Mofijul</au><au>Saha, Sanjay</au><au>Rahman, Md Mahmudur</au><au>Shatabda, Swakkhar</au><au>Farid, Dewan Md</au><au>Dehzangi, Abdollah</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features</atitle><jtitle>Proteins, structure, function, and bioinformatics</jtitle><addtitle>Proteins</addtitle><date>2018-07</date><risdate>2018</risdate><volume>86</volume><issue>7</issue><spage>777</spage><epage>789</epage><pages>777-789</pages><issn>0887-3585</issn><eissn>1097-0134</eissn><abstract>Glycation is chemical reaction by which sugar molecule bonds with a protein without the help of enzymes. This is often cause to many diseases and therefore the knowledge about glycation is very important. In this paper, we present iProtGly‐SS, a protein lysine glycation site identification method based on features extracted from sequence and secondary structural information. In the experiments, we found the best feature groups combination: Amino Acid Composition, Secondary Structure Motifs, and Polarity. We used support vector machine classifier to train our model and used an optimal set of features using a group based forward feature selection technique. On standard benchmark datasets, our method is able to significantly outperform existing methods for glycation prediction. A web server for iProtGly‐SS is implemented and publicly available to use: http://brl.uiu.ac.bd/iprotgly-ss/.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>29675975</pmid><doi>10.1002/prot.25511</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-0669-072X</orcidid></addata></record> |
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subjects | Amino acid composition Amino acid sequence Chemical bonds Chemical reactions classification evolutionary features Feature extraction feature selection Glycosylation Internet Lysine Molecular chains Organic chemistry Polarity protein glycation Protein structure Proteins Secondary structure Servers structural features Sugar Support vector machines |
title | iProtGly‐SS: Identifying protein glycation sites using sequence and structure based features |
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