Molecular evaluation using in silico protein interaction profiles
To find a correlation between the activities and structures of molecules is one of the most important subjects for molecular evaluation study. Traditional quantitative structure-activity relationship (QSAR) methodologies represent those attempts using physicochemical descriptors. Creating a new mole...
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Veröffentlicht in: | Bioinformatics 2003-08, Vol.19 (12), p.1514-1523 |
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container_title | Bioinformatics |
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creator | HAYASHI, Yosninaru SAKAGUCHI, Katsuyoshi KOBAYASHI, Mime KOBAYASHI, Masaki KIKUCHI, Yo ICHIISHI, Eiichiro |
description | To find a correlation between the activities and structures of molecules is one of the most important subjects for molecular evaluation study. Traditional quantitative structure-activity relationship (QSAR) methodologies represent those attempts using physicochemical descriptors. Creating a new molecular description factor based on the results of a computational docking study will add new dimensions to molecular evaluation.
We propose a new molecular description factor analysis system called the Comparative Molecular Interaction Profile Analysis (CoMIPA) system in which the AutoDock program is used for docking evaluation of small molecule compound-protein complexes. Interaction energies are calculated, and the data sets obtained are called interaction profiles (IPFs). Using the IPF as a scoring indicator, the system could be a powerful tool to cluster the interacting properties between small molecules and bio macromolecules such as ligand-receptor bindings. Further development of the system will enable us to predict the adverse effects of a drug candidate. |
doi_str_mv | 10.1093/bioinformatics/btg189 |
format | Article |
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We propose a new molecular description factor analysis system called the Comparative Molecular Interaction Profile Analysis (CoMIPA) system in which the AutoDock program is used for docking evaluation of small molecule compound-protein complexes. Interaction energies are calculated, and the data sets obtained are called interaction profiles (IPFs). Using the IPF as a scoring indicator, the system could be a powerful tool to cluster the interacting properties between small molecules and bio macromolecules such as ligand-receptor bindings. Further development of the system will enable us to predict the adverse effects of a drug candidate.</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1367-4811</identifier><identifier>EISSN: 1460-2059</identifier><identifier>DOI: 10.1093/bioinformatics/btg189</identifier><identifier>PMID: 12912832</identifier><identifier>CODEN: BOINFP</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>Algorithms ; Binding Sites ; Biological and medical sciences ; Computer Simulation ; Energy Transfer ; Fundamental and applied biological sciences. Psychology ; General aspects ; Macromolecular Substances ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Models, Chemical ; Models, Molecular ; Protein Array Analysis - methods ; Protein Binding ; Protein Interaction Mapping - methods ; Proteins - chemistry ; Proteins - classification ; Quantitative Structure-Activity Relationship</subject><ispartof>Bioinformatics, 2003-08, Vol.19 (12), p.1514-1523</ispartof><rights>Copyright Oxford University Press(England) Aug 12, 2003</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c440t-c66fff99ddcfddaf78696410394e5f372897b1a976b3fad31f160e539676fe873</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15900932$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/12912832$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>HAYASHI, Yosninaru</creatorcontrib><creatorcontrib>SAKAGUCHI, Katsuyoshi</creatorcontrib><creatorcontrib>KOBAYASHI, Mime</creatorcontrib><creatorcontrib>KOBAYASHI, Masaki</creatorcontrib><creatorcontrib>KIKUCHI, Yo</creatorcontrib><creatorcontrib>ICHIISHI, Eiichiro</creatorcontrib><title>Molecular evaluation using in silico protein interaction profiles</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>To find a correlation between the activities and structures of molecules is one of the most important subjects for molecular evaluation study. Traditional quantitative structure-activity relationship (QSAR) methodologies represent those attempts using physicochemical descriptors. Creating a new molecular description factor based on the results of a computational docking study will add new dimensions to molecular evaluation.
We propose a new molecular description factor analysis system called the Comparative Molecular Interaction Profile Analysis (CoMIPA) system in which the AutoDock program is used for docking evaluation of small molecule compound-protein complexes. Interaction energies are calculated, and the data sets obtained are called interaction profiles (IPFs). Using the IPF as a scoring indicator, the system could be a powerful tool to cluster the interacting properties between small molecules and bio macromolecules such as ligand-receptor bindings. Further development of the system will enable us to predict the adverse effects of a drug candidate.</description><subject>Algorithms</subject><subject>Binding Sites</subject><subject>Biological and medical sciences</subject><subject>Computer Simulation</subject><subject>Energy Transfer</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>Macromolecular Substances</subject><subject>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</subject><subject>Models, Chemical</subject><subject>Models, Molecular</subject><subject>Protein Array Analysis - methods</subject><subject>Protein Binding</subject><subject>Protein Interaction Mapping - methods</subject><subject>Proteins - chemistry</subject><subject>Proteins - classification</subject><subject>Quantitative Structure-Activity Relationship</subject><issn>1367-4803</issn><issn>1367-4811</issn><issn>1460-2059</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkV1PwyAUhonRuDn9CZrGRO_qoBQKl8viVzLjjV43lMLCQmFCa-K_l7lGozdecSDPeU44LwDnCN4gyPG8Md447UMneiPjvOnXiPEDMEWYVnnJEDr8riGegJMYNxBCAgk9BhNUcFQwXEzB4slbJQcrQqbehR2SzbtsiMatM-OyaKyRPtsG36t0Na5XQcgvJr1pY1U8BUda2KjOxnMGXu9uX5YP-er5_nG5WOWyLGGfS0q11py3rdRtK3TFKKclgpiXimhcFYxXDRK8og3WosVIIwoVwZxWVCtW4Rm43nvT4LdBxb7uTJTKWuGUH2JdYUJLSPG_IOKIYU524OUfcOOH4NInEsMoKZMwQWQPyeBjDErX22A6ET5qBOtdEvXvJOp9EqnvYpQPTafan65x9Qm4GgERpbA6CCdN_OEIh8le4E802ZcE</recordid><startdate>20030812</startdate><enddate>20030812</enddate><creator>HAYASHI, Yosninaru</creator><creator>SAKAGUCHI, Katsuyoshi</creator><creator>KOBAYASHI, Mime</creator><creator>KOBAYASHI, Masaki</creator><creator>KIKUCHI, Yo</creator><creator>ICHIISHI, Eiichiro</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>IQODW</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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20030812</creationdate><title>Molecular evaluation using in silico protein interaction profiles</title><author>HAYASHI, Yosninaru ; SAKAGUCHI, Katsuyoshi ; KOBAYASHI, Mime ; KOBAYASHI, Masaki ; KIKUCHI, Yo ; ICHIISHI, Eiichiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c440t-c66fff99ddcfddaf78696410394e5f372897b1a976b3fad31f160e539676fe873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Algorithms</topic><topic>Binding Sites</topic><topic>Biological and medical sciences</topic><topic>Computer Simulation</topic><topic>Energy Transfer</topic><topic>Fundamental and applied biological sciences. 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Data processing in biology (general aspects)</topic><topic>Models, Chemical</topic><topic>Models, Molecular</topic><topic>Protein Array Analysis - methods</topic><topic>Protein Binding</topic><topic>Protein Interaction Mapping - methods</topic><topic>Proteins - chemistry</topic><topic>Proteins - classification</topic><topic>Quantitative Structure-Activity Relationship</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>HAYASHI, Yosninaru</creatorcontrib><creatorcontrib>SAKAGUCHI, Katsuyoshi</creatorcontrib><creatorcontrib>KOBAYASHI, Mime</creatorcontrib><creatorcontrib>KOBAYASHI, Masaki</creatorcontrib><creatorcontrib>KIKUCHI, Yo</creatorcontrib><creatorcontrib>ICHIISHI, Eiichiro</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>HAYASHI, Yosninaru</au><au>SAKAGUCHI, Katsuyoshi</au><au>KOBAYASHI, Mime</au><au>KOBAYASHI, Masaki</au><au>KIKUCHI, Yo</au><au>ICHIISHI, Eiichiro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Molecular evaluation using in silico protein interaction profiles</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2003-08-12</date><risdate>2003</risdate><volume>19</volume><issue>12</issue><spage>1514</spage><epage>1523</epage><pages>1514-1523</pages><issn>1367-4803</issn><eissn>1367-4811</eissn><eissn>1460-2059</eissn><coden>BOINFP</coden><abstract>To find a correlation between the activities and structures of molecules is one of the most important subjects for molecular evaluation study. Traditional quantitative structure-activity relationship (QSAR) methodologies represent those attempts using physicochemical descriptors. Creating a new molecular description factor based on the results of a computational docking study will add new dimensions to molecular evaluation.
We propose a new molecular description factor analysis system called the Comparative Molecular Interaction Profile Analysis (CoMIPA) system in which the AutoDock program is used for docking evaluation of small molecule compound-protein complexes. Interaction energies are calculated, and the data sets obtained are called interaction profiles (IPFs). Using the IPF as a scoring indicator, the system could be a powerful tool to cluster the interacting properties between small molecules and bio macromolecules such as ligand-receptor bindings. Further development of the system will enable us to predict the adverse effects of a drug candidate.</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>12912832</pmid><doi>10.1093/bioinformatics/btg189</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Binding Sites Biological and medical sciences Computer Simulation Energy Transfer Fundamental and applied biological sciences. Psychology General aspects Macromolecular Substances Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Chemical Models, Molecular Protein Array Analysis - methods Protein Binding Protein Interaction Mapping - methods Proteins - chemistry Proteins - classification Quantitative Structure-Activity Relationship |
title | Molecular evaluation using in silico protein interaction profiles |
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