Automatic generation of bioinformatics tools for predicting protein-ligand binding sites
Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can...
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| Veröffentlicht in: | Bioinformatics 2016-03, Vol.32 (6), p.901-907 |
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| container_title | Bioinformatics |
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| creator | Komiyama, Yusuke Banno, Masaki Ueki, Kokoro Saad, Gul Shimizu, Kentaro |
| description | Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.
We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.
The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.
shimizu@bi.a.u-tokyo.ac.jp
Supplementary data are available at Bioinformatics online. |
| doi_str_mv | 10.1093/bioinformatics/btv593 |
| format | Article |
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We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.
The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.
shimizu@bi.a.u-tokyo.ac.jp
Supplementary data are available at Bioinformatics online.</description><identifier>ISSN: 1367-4803</identifier><identifier>ISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>EISSN: 1460-2059</identifier><identifier>DOI: 10.1093/bioinformatics/btv593</identifier><identifier>PMID: 26545824</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Applications programs ; artificial intelligence ; Automation ; Binding ; Binding Sites ; Bioinformatics ; Computational Biology ; computer software ; drug design ; Internet ; Ligands ; Mathematical models ; Original Papers ; Pipelines ; prediction ; Protein Binding ; Proteins - metabolism ; Semantics ; Software ; world wide web</subject><ispartof>Bioinformatics, 2016-03, Vol.32 (6), p.901-907</ispartof><rights>The Author 2015. Published by Oxford University Press.</rights><rights>The Author 2015. Published by Oxford University Press. 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c510t-2ef33938865d71bc56d9cf8af3ba76dec4f4d5fd4fc7c496fb33a7f7b00583013</citedby><cites>FETCH-LOGICAL-c510t-2ef33938865d71bc56d9cf8af3ba76dec4f4d5fd4fc7c496fb33a7f7b00583013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803387/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4803387/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26545824$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Komiyama, Yusuke</creatorcontrib><creatorcontrib>Banno, Masaki</creatorcontrib><creatorcontrib>Ueki, Kokoro</creatorcontrib><creatorcontrib>Saad, Gul</creatorcontrib><creatorcontrib>Shimizu, Kentaro</creatorcontrib><title>Automatic generation of bioinformatics tools for predicting protein-ligand binding sites</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.
We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.
The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.
shimizu@bi.a.u-tokyo.ac.jp
Supplementary data are available at Bioinformatics online.</description><subject>Algorithms</subject><subject>Applications programs</subject><subject>artificial intelligence</subject><subject>Automation</subject><subject>Binding</subject><subject>Binding Sites</subject><subject>Bioinformatics</subject><subject>Computational Biology</subject><subject>computer software</subject><subject>drug design</subject><subject>Internet</subject><subject>Ligands</subject><subject>Mathematical models</subject><subject>Original Papers</subject><subject>Pipelines</subject><subject>prediction</subject><subject>Protein Binding</subject><subject>Proteins - metabolism</subject><subject>Semantics</subject><subject>Software</subject><subject>world wide web</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><issn>1460-2059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNUktLAzEYDKJoffwEZY9eVvPcZC-CFF8geFHwFrJ5rJFtUpNU8N-7tVrsSU-ZfJkZ-CYDwDGCZwi25Lzz0QcX00wVr_N5V95ZS7bABJGG11QgtL3GkOyB_ZxfIYQMsmYX7OGGUSYwnYDny0WJXx5Vb4NNI4qhiq7a9K9KjEOuxns1T9Z4XXzoRxiL9aEefK-CGSXBLMfZF5sPwY5TQ7ZH3-cBeLq-epze1vcPN3fTy_taMwRLja0jpCVCNMxw1GnWmFY7oRzpFG-M1dRRw5yhTnNN28Z1hCjueDfuIghE5ABcrHzni25mjbahJDXIefIzlT5kVF5uvgT_Ivv4Lpe5EMFHg9NvgxTfFjYXOfNZ22FQwcZFlhgzghmjtP2TisRXwrjBf1O54JRziOF_qJC2LYZspLIVVaeYc7JuvSeCclkLufltclWLUXfyO6S16qcH5BM47Lup</recordid><startdate>20160315</startdate><enddate>20160315</enddate><creator>Komiyama, Yusuke</creator><creator>Banno, Masaki</creator><creator>Ueki, Kokoro</creator><creator>Saad, Gul</creator><creator>Shimizu, Kentaro</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>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><scope>7SC</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope></search><sort><creationdate>20160315</creationdate><title>Automatic generation of bioinformatics tools for predicting protein-ligand binding sites</title><author>Komiyama, Yusuke ; Banno, Masaki ; Ueki, Kokoro ; Saad, Gul ; Shimizu, Kentaro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c510t-2ef33938865d71bc56d9cf8af3ba76dec4f4d5fd4fc7c496fb33a7f7b00583013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Applications programs</topic><topic>artificial intelligence</topic><topic>Automation</topic><topic>Binding</topic><topic>Binding Sites</topic><topic>Bioinformatics</topic><topic>Computational Biology</topic><topic>computer software</topic><topic>drug design</topic><topic>Internet</topic><topic>Ligands</topic><topic>Mathematical models</topic><topic>Original Papers</topic><topic>Pipelines</topic><topic>prediction</topic><topic>Protein Binding</topic><topic>Proteins - metabolism</topic><topic>Semantics</topic><topic>Software</topic><topic>world wide web</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Komiyama, Yusuke</creatorcontrib><creatorcontrib>Banno, Masaki</creatorcontrib><creatorcontrib>Ueki, Kokoro</creatorcontrib><creatorcontrib>Saad, Gul</creatorcontrib><creatorcontrib>Shimizu, Kentaro</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Komiyama, Yusuke</au><au>Banno, Masaki</au><au>Ueki, Kokoro</au><au>Saad, Gul</au><au>Shimizu, Kentaro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic generation of bioinformatics tools for predicting protein-ligand binding sites</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2016-03-15</date><risdate>2016</risdate><volume>32</volume><issue>6</issue><spage>901</spage><epage>907</epage><pages>901-907</pages><issn>1367-4803</issn><issn>1460-2059</issn><eissn>1367-4811</eissn><eissn>1460-2059</eissn><abstract>Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.
We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.
The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.
shimizu@bi.a.u-tokyo.ac.jp
Supplementary data are available at Bioinformatics online.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>26545824</pmid><doi>10.1093/bioinformatics/btv593</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Bioinformatics, 2016-03, Vol.32 (6), p.901-907 |
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| source | Oxford Journals Open Access Collection; MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Alma/SFX Local Collection |
| subjects | Algorithms Applications programs artificial intelligence Automation Binding Binding Sites Bioinformatics Computational Biology computer software drug design Internet Ligands Mathematical models Original Papers Pipelines prediction Protein Binding Proteins - metabolism Semantics Software world wide web |
| title | Automatic generation of bioinformatics tools for predicting protein-ligand binding sites |
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