Protein-based virtual screening of chemical databases. II. Are homology models of g-protein coupled receptors suitable targets?
The aim of the current study is to investigate whether homology models of G‐Protein‐Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 Å‐resolution X‐ray structure of bovine...
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Veröffentlicht in: | Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2003-01, Vol.50 (1), p.5-25 |
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description | The aim of the current study is to investigate whether homology models of G‐Protein‐Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 Å‐resolution X‐ray structure of bovine rhodopsin, homology models of an “antagonist‐bound” form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three‐dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin‐based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge‐ and pharmacophore‐based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist‐bound and agonist‐bound states). Proteins 2003;50:5–25. © 2002 Wiley‐Liss, Inc. |
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II. Are homology models of g-protein coupled receptors suitable targets?</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Bissantz, Caterina ; Bernard, Philippe ; Hibert, Marcel ; Rognan, Didier</creator><creatorcontrib>Bissantz, Caterina ; Bernard, Philippe ; Hibert, Marcel ; Rognan, Didier</creatorcontrib><description>The aim of the current study is to investigate whether homology models of G‐Protein‐Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 Å‐resolution X‐ray structure of bovine rhodopsin, homology models of an “antagonist‐bound” form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three‐dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin‐based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge‐ and pharmacophore‐based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist‐bound and agonist‐bound states). 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II. Are homology models of g-protein coupled receptors suitable targets?</title><title>Proteins, structure, function, and bioinformatics</title><addtitle>Proteins</addtitle><description>The aim of the current study is to investigate whether homology models of G‐Protein‐Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 Å‐resolution X‐ray structure of bovine rhodopsin, homology models of an “antagonist‐bound” form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three‐dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin‐based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge‐ and pharmacophore‐based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist‐bound and agonist‐bound states). Proteins 2003;50:5–25. © 2002 Wiley‐Liss, Inc.</description><subject>Adrenergic beta-2 Receptor Agonists</subject><subject>Algorithms</subject><subject>Amino Acid Sequence</subject><subject>Animals</subject><subject>Antidiuretic Hormone Receptor Antagonists</subject><subject>Cattle</subject><subject>Computational Biology - methods</subject><subject>Computer Simulation</subject><subject>Databases, Factual</subject><subject>docking</subject><subject>Dopamine D2 Receptor Antagonists</subject><subject>Drug Delivery Systems</subject><subject>GPCRs</subject><subject>Heterotrimeric GTP-Binding Proteins - metabolism</subject><subject>homology modeling</subject><subject>Humans</subject><subject>Ligands</subject><subject>Models, Molecular</subject><subject>Molecular Sequence Data</subject><subject>Receptor, Muscarinic M1</subject><subject>Receptors, Adrenergic, beta-2 - chemistry</subject><subject>Receptors, Cell Surface - agonists</subject><subject>Receptors, Cell Surface - antagonists & inhibitors</subject><subject>Receptors, Cell Surface - chemistry</subject><subject>Receptors, Dopamine D2 - agonists</subject><subject>Receptors, Dopamine D2 - chemistry</subject><subject>Receptors, Dopamine D3</subject><subject>Receptors, Muscarinic - chemistry</subject><subject>Receptors, Opioid, delta - agonists</subject><subject>Receptors, Opioid, delta - chemistry</subject><subject>Receptors, Vasopressin - chemistry</subject><subject>Rhodopsin - chemistry</subject><subject>scoring</subject><subject>Sequence Alignment</subject><subject>Sequence Homology, Amino Acid</subject><subject>structure-based ligand design</subject><issn>0887-3585</issn><issn>1097-0134</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kM1u1DAURi0EokNhwwMgr1hUytQ_cWyvUDWi7aCKllLE0nKSm6nBGQc7AWbFq-NpBtix8tXV-Y58P4ReUrKkhLDTIYYxT4zLR2hBiZYFobx8jBZEKVlwocQRepbSF0JIpXn1FB1RVkoqtFigXzc5DG5b1DZBi7-7OE7W49REgK3bbnDocHMPvWvytrWj3XNpidfrJT6LgO9DH3zY7HAfWvBpj2-KYXbiJkyDz9YIDQxjiAmnyWWFBzzauIExvXmOnnTWJ3hxeI_Rp_O3d6vL4ur6Yr06uyqakuWDNJetpgJK0lacAis1qUpgtehkDR1vKq1ACyVB2HyzzXtba8VYVwtd1xXwY_R69ua_fZsgjaZ3qQHv7RbClIxkSjHFeQZPZrCJIaUInRmi623cGUrMvm6zv8481J3hVwfrVPfQ_kMP_WaAzsAP52H3H5W5ub2--yMt5oxLI_z8m7Hxq6kkl8J8fn9h1MdydfthdWne8d_xRpwF</recordid><startdate>20030101</startdate><enddate>20030101</enddate><creator>Bissantz, Caterina</creator><creator>Bernard, Philippe</creator><creator>Hibert, Marcel</creator><creator>Rognan, Didier</creator><general>Wiley Subscription Services, Inc., A Wiley Company</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>7X8</scope></search><sort><creationdate>20030101</creationdate><title>Protein-based virtual screening of chemical databases. II. Are homology models of g-protein coupled receptors suitable targets?</title><author>Bissantz, Caterina ; Bernard, Philippe ; Hibert, Marcel ; Rognan, Didier</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4297-937d915e40d631e249064e2b5f7bef3c698e9587e5a887ab5fab9822fb59bb6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Adrenergic beta-2 Receptor Agonists</topic><topic>Algorithms</topic><topic>Amino Acid Sequence</topic><topic>Animals</topic><topic>Antidiuretic Hormone Receptor Antagonists</topic><topic>Cattle</topic><topic>Computational Biology - methods</topic><topic>Computer Simulation</topic><topic>Databases, Factual</topic><topic>docking</topic><topic>Dopamine D2 Receptor Antagonists</topic><topic>Drug Delivery Systems</topic><topic>GPCRs</topic><topic>Heterotrimeric GTP-Binding Proteins - metabolism</topic><topic>homology modeling</topic><topic>Humans</topic><topic>Ligands</topic><topic>Models, Molecular</topic><topic>Molecular Sequence Data</topic><topic>Receptor, Muscarinic M1</topic><topic>Receptors, Adrenergic, beta-2 - chemistry</topic><topic>Receptors, Cell Surface - agonists</topic><topic>Receptors, Cell Surface - antagonists & inhibitors</topic><topic>Receptors, Cell Surface - chemistry</topic><topic>Receptors, Dopamine D2 - agonists</topic><topic>Receptors, Dopamine D2 - chemistry</topic><topic>Receptors, Dopamine D3</topic><topic>Receptors, Muscarinic - chemistry</topic><topic>Receptors, Opioid, delta - agonists</topic><topic>Receptors, Opioid, delta - chemistry</topic><topic>Receptors, Vasopressin - chemistry</topic><topic>Rhodopsin - chemistry</topic><topic>scoring</topic><topic>Sequence Alignment</topic><topic>Sequence Homology, Amino Acid</topic><topic>structure-based ligand design</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bissantz, Caterina</creatorcontrib><creatorcontrib>Bernard, Philippe</creatorcontrib><creatorcontrib>Hibert, Marcel</creatorcontrib><creatorcontrib>Rognan, Didier</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>MEDLINE - Academic</collection><jtitle>Proteins, structure, function, and bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bissantz, Caterina</au><au>Bernard, Philippe</au><au>Hibert, Marcel</au><au>Rognan, Didier</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Protein-based virtual screening of chemical databases. II. Are homology models of g-protein coupled receptors suitable targets?</atitle><jtitle>Proteins, structure, function, and bioinformatics</jtitle><addtitle>Proteins</addtitle><date>2003-01-01</date><risdate>2003</risdate><volume>50</volume><issue>1</issue><spage>5</spage><epage>25</epage><pages>5-25</pages><issn>0887-3585</issn><eissn>1097-0134</eissn><abstract>The aim of the current study is to investigate whether homology models of G‐Protein‐Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 Å‐resolution X‐ray structure of bovine rhodopsin, homology models of an “antagonist‐bound” form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three‐dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin‐based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge‐ and pharmacophore‐based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist‐bound and agonist‐bound states). Proteins 2003;50:5–25. © 2002 Wiley‐Liss, Inc.</abstract><cop>New York</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><pmid>12471595</pmid><doi>10.1002/prot.10237</doi><tpages>21</tpages></addata></record> |
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subjects | Adrenergic beta-2 Receptor Agonists Algorithms Amino Acid Sequence Animals Antidiuretic Hormone Receptor Antagonists Cattle Computational Biology - methods Computer Simulation Databases, Factual docking Dopamine D2 Receptor Antagonists Drug Delivery Systems GPCRs Heterotrimeric GTP-Binding Proteins - metabolism homology modeling Humans Ligands Models, Molecular Molecular Sequence Data Receptor, Muscarinic M1 Receptors, Adrenergic, beta-2 - chemistry Receptors, Cell Surface - agonists Receptors, Cell Surface - antagonists & inhibitors Receptors, Cell Surface - chemistry Receptors, Dopamine D2 - agonists Receptors, Dopamine D2 - chemistry Receptors, Dopamine D3 Receptors, Muscarinic - chemistry Receptors, Opioid, delta - agonists Receptors, Opioid, delta - chemistry Receptors, Vasopressin - chemistry Rhodopsin - chemistry scoring Sequence Alignment Sequence Homology, Amino Acid structure-based ligand design |
title | Protein-based virtual screening of chemical databases. II. Are homology models of g-protein coupled receptors suitable targets? |
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