Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates
This chapter discusses experience in homology modeling of cytochrome P450 (CYPs) 2C8, 2C9, 2C18, and CYP2C19 based on the rabbit CYP2C5 crystal structure. A substrate selectivity analysis for the CYP2C subfamily is also discussed in the chapter and highlights the amino acids responsible for the sele...
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Veröffentlicht in: | Methods in Enzymology 2002, Vol.357, p.133-144 |
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creator | Masimirembwa, Collen M. Ridderström, Marianne Zamora, Ismael Andersson, Tommy B. |
description | This chapter discusses experience in homology modeling of cytochrome P450 (CYPs) 2C8, 2C9, 2C18, and CYP2C19 based on the rabbit CYP2C5 crystal structure. A substrate selectivity analysis for the CYP2C subfamily is also discussed in the chapter and highlights the amino acids responsible for the selectivity. Generation of a three dimension-quantitative structure–activity relationship (QSAR) model for a diverse set of CYP2C9 inhibitors taking into account important parameters, such as mechanism of inhibition and stereochemistry, is described in the chapter. Basic validation of the QSAR models involves cross validation using the “leave one out” (L.O.O.) technique or different percentages of elements of the original training set and trying to predict their biological effect by the model generated with the remaining compounds. This method evaluates the predictive power of the model inside the set defined to build it but it could give an overly optimistic view of the performance of the model. |
doi_str_mv | 10.1016/S0076-6879(02)57673-4 |
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A substrate selectivity analysis for the CYP2C subfamily is also discussed in the chapter and highlights the amino acids responsible for the selectivity. Generation of a three dimension-quantitative structure–activity relationship (QSAR) model for a diverse set of CYP2C9 inhibitors taking into account important parameters, such as mechanism of inhibition and stereochemistry, is described in the chapter. Basic validation of the QSAR models involves cross validation using the “leave one out” (L.O.O.) technique or different percentages of elements of the original training set and trying to predict their biological effect by the model generated with the remaining compounds. This method evaluates the predictive power of the model inside the set defined to build it but it could give an overly optimistic view of the performance of the model.</description><subject>Animals</subject><subject>Anti-Inflammatory Agents, Non-Steroidal - chemistry</subject><subject>Anti-Inflammatory Agents, Non-Steroidal - metabolism</subject><subject>Binding Sites</subject><subject>Cytochrome P-450 Enzyme Inhibitors</subject><subject>Cytochrome P-450 Enzyme System - chemistry</subject><subject>Cytochrome P-450 Enzyme System - metabolism</subject><subject>Diclofenac - chemistry</subject><subject>Diclofenac - metabolism</subject><subject>Ligands</subject><subject>Models, Molecular</subject><subject>Multigene Family</subject><subject>Protein Binding</subject><subject>Protein Structure, Tertiary</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Rabbits</subject><subject>Reproducibility of Results</subject><issn>0076-6879</issn><issn>1557-7988</issn><isbn>0121822605</isbn><isbn>9780121822606</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo9kctuFTEMhiMooofSRwDNCsFiinPPrBA6KhepUpHoAlZRknE5gTOTaZKh6tsz50K9sC3r8y_ZPyGvKFxQoOr9dwCtWmV09xbYO6mV5q14QlZUSt3qzpin5AVQRg1jCuQJWT3yp-S8lN-wBOWCG_WcnFImmOhArsiPdRp8HOP4q5k2Lg8upGmTMjZu7Jspp4pxbIbU43aH1LTMsI-hNuuf34SEJo6b6GNNuew3yuxLza5ieUme3bptwfNjPSM3ny5v1l_aq-vPX9cfr9rAtakt89oFRkF4DBhk1wWqekE9pdRzzeG218LQrmMuGOScgzAaFGOUBwCp-BlpD7LlHqfZ2ynHweUHm1y0x9GfpUMrOwaw498c-OW2uxlLtUMsAbdbN2Kai9VMabGkBXx9BGc_YP8o_P91C_DhAOBy3d-I2ZYQcQzLezKGavsULQW7M8_uzbM7MywwuzfPCv4PXjWIFA</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Masimirembwa, Collen M.</creator><creator>Ridderström, Marianne</creator><creator>Zamora, Ismael</creator><creator>Andersson, Tommy B.</creator><general>Elsevier Science & Technology</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>ADTPV</scope><scope>AOWAS</scope></search><sort><creationdate>2002</creationdate><title>Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates</title><author>Masimirembwa, Collen M. ; Ridderström, Marianne ; Zamora, Ismael ; Andersson, Tommy B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c378t-2b7ac2104becec599c16d41b111b3730fd7481992ac8e3330487062213c00563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Animals</topic><topic>Anti-Inflammatory Agents, Non-Steroidal - chemistry</topic><topic>Anti-Inflammatory Agents, Non-Steroidal - metabolism</topic><topic>Binding Sites</topic><topic>Cytochrome P-450 Enzyme Inhibitors</topic><topic>Cytochrome P-450 Enzyme System - chemistry</topic><topic>Cytochrome P-450 Enzyme System - metabolism</topic><topic>Diclofenac - chemistry</topic><topic>Diclofenac - metabolism</topic><topic>Ligands</topic><topic>Models, Molecular</topic><topic>Multigene Family</topic><topic>Protein Binding</topic><topic>Protein Structure, Tertiary</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Rabbits</topic><topic>Reproducibility of Results</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Masimirembwa, Collen M.</creatorcontrib><creatorcontrib>Ridderström, Marianne</creatorcontrib><creatorcontrib>Zamora, Ismael</creatorcontrib><creatorcontrib>Andersson, Tommy B.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>SwePub</collection><collection>SwePub Articles</collection><jtitle>Methods in Enzymology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Masimirembwa, Collen M.</au><au>Ridderström, Marianne</au><au>Zamora, Ismael</au><au>Andersson, Tommy B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates</atitle><jtitle>Methods in Enzymology</jtitle><addtitle>Methods Enzymol</addtitle><date>2002</date><risdate>2002</risdate><volume>357</volume><spage>133</spage><epage>144</epage><pages>133-144</pages><issn>0076-6879</issn><eissn>1557-7988</eissn><isbn>0121822605</isbn><isbn>9780121822606</isbn><abstract>This chapter discusses experience in homology modeling of cytochrome P450 (CYPs) 2C8, 2C9, 2C18, and CYP2C19 based on the rabbit CYP2C5 crystal structure. 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subjects | Animals Anti-Inflammatory Agents, Non-Steroidal - chemistry Anti-Inflammatory Agents, Non-Steroidal - metabolism Binding Sites Cytochrome P-450 Enzyme Inhibitors Cytochrome P-450 Enzyme System - chemistry Cytochrome P-450 Enzyme System - metabolism Diclofenac - chemistry Diclofenac - metabolism Ligands Models, Molecular Multigene Family Protein Binding Protein Structure, Tertiary Quantitative Structure-Activity Relationship Rabbits Reproducibility of Results |
title | Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates |
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