A novel strategy for the structure-based drug design of heat shock protein 90 inhibitors
Heat shock protein 90 (HSP90) regulates the correct folding of nascent protein in tumor cells. Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study....
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description | Heat shock protein 90 (HSP90) regulates the correct folding of nascent protein in tumor cells. Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the template (PDB code: 1uy7). Six scoring functions (DockScore, PLP1, PLP2, LigScore1, LigScore2, and PMF) were employed to evaluate the binding affinity. The correlation coefficients (r 2 ) between each scoring function and the biological activity were used to determine the accurate scoring function for virtual screening. The r 2 values were 0.878, 0.696, 0.395, 0.276, 0.050, and 0.187 for DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF, respectively. According to the accurate DockScore, most of flavonoids and YC-1 derivatives had the higher binding affinities to HSP90 than controls and built the important hydrogen bond with the key residue ASP93. The structure-based de novo design by using Ludi program was performed to increase the binding affinity. Final thirteen potential compounds had higher binding affinity than the original ones. These candidates might guide drug design for novel HSP90 inhibitors in the future. |
doi_str_mv | 10.1109/IJCNN.2008.4633952 |
format | Conference Proceeding |
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Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the template (PDB code: 1uy7). Six scoring functions (DockScore, PLP1, PLP2, LigScore1, LigScore2, and PMF) were employed to evaluate the binding affinity. The correlation coefficients (r 2 ) between each scoring function and the biological activity were used to determine the accurate scoring function for virtual screening. The r 2 values were 0.878, 0.696, 0.395, 0.276, 0.050, and 0.187 for DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF, respectively. According to the accurate DockScore, most of flavonoids and YC-1 derivatives had the higher binding affinities to HSP90 than controls and built the important hydrogen bond with the key residue ASP93. The structure-based de novo design by using Ludi program was performed to increase the binding affinity. Final thirteen potential compounds had higher binding affinity than the original ones. 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Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the template (PDB code: 1uy7). Six scoring functions (DockScore, PLP1, PLP2, LigScore1, LigScore2, and PMF) were employed to evaluate the binding affinity. The correlation coefficients (r 2 ) between each scoring function and the biological activity were used to determine the accurate scoring function for virtual screening. The r 2 values were 0.878, 0.696, 0.395, 0.276, 0.050, and 0.187 for DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF, respectively. According to the accurate DockScore, most of flavonoids and YC-1 derivatives had the higher binding affinities to HSP90 than controls and built the important hydrogen bond with the key residue ASP93. The structure-based de novo design by using Ludi program was performed to increase the binding affinity. Final thirteen potential compounds had higher binding affinity than the original ones. These candidates might guide drug design for novel HSP90 inhibitors in the future.</description><subject>Artificial neural networks</subject><subject>Compounds</subject><subject>Correlation</subject><subject>Crystals</subject><subject>Inhibitors</subject><subject>Joints</subject><subject>Proteins</subject><issn>2161-4393</issn><issn>1522-4899</issn><issn>2161-4407</issn><isbn>1424418208</isbn><isbn>9781424418206</isbn><isbn>9781424432196</isbn><isbn>1424432197</isbn><isbn>1424418216</isbn><isbn>9781424418213</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNqFkEtPwzAQhM1Loi38Abj4xC1l_YgTH1HFo6gqF5C4RY6zaQxtUmwHqf-eVi3iyGVWmvm0Gg0hVwzGjIG-nT5P5vMxB8jHUgmhU35EhkxyKVnOmTomg62yRErITv4CyE9_A6HFORmG8AHAhdZiQN7vaNt945KG6E3ExYbWnaexwZ3R29h7TEoTsKKV7xe0wuAWLe1q2qCJNDSd_aRr30V0LdVAXdu40sXOhwtyVptlwMvDHZG3h_vXyVMye3mcTu5mieMii4lF0MyiUBKrFExtUJRVDnmaa8s02jwrS6stIG4bK0STGZUh8opLZAqUGJGb_d9ti68eQyxWLlhcLk2LXR8KIVMGIsv-BTkolaZ8B17vQYeIxdq7lfGb4jC4-AEbsHEY</recordid><startdate>20080601</startdate><enddate>20080601</enddate><creator>Chen, OY-C</creator><creator>Chen, Guan-Wen</creator><creator>Chen, WY-C</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7SC</scope><scope>7SP</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20080601</creationdate><title>A novel strategy for the structure-based drug design of heat shock protein 90 inhibitors</title><author>Chen, OY-C ; Chen, Guan-Wen ; Chen, WY-C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i237t-ce091ce364ed50afae3bd808589c19ec87bbc9c0ee0236eea7a67ee2d24e16063</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Artificial neural networks</topic><topic>Compounds</topic><topic>Correlation</topic><topic>Crystals</topic><topic>Inhibitors</topic><topic>Joints</topic><topic>Proteins</topic><toplevel>online_resources</toplevel><creatorcontrib>Chen, OY-C</creatorcontrib><creatorcontrib>Chen, Guan-Wen</creatorcontrib><creatorcontrib>Chen, WY-C</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, OY-C</au><au>Chen, Guan-Wen</au><au>Chen, WY-C</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A novel strategy for the structure-based drug design of heat shock protein 90 inhibitors</atitle><btitle>2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)</btitle><stitle>IJCNN</stitle><date>2008-06-01</date><risdate>2008</risdate><volume>10</volume><spage>1199</spage><epage>1206</epage><pages>1199-1206</pages><issn>2161-4393</issn><issn>1522-4899</issn><eissn>2161-4407</eissn><isbn>1424418208</isbn><isbn>9781424418206</isbn><isbn>9781424432196</isbn><isbn>1424432197</isbn><eisbn>1424418216</eisbn><eisbn>9781424418213</eisbn><abstract>Heat shock protein 90 (HSP90) regulates the correct folding of nascent protein in tumor cells. Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the template (PDB code: 1uy7). Six scoring functions (DockScore, PLP1, PLP2, LigScore1, LigScore2, and PMF) were employed to evaluate the binding affinity. The correlation coefficients (r 2 ) between each scoring function and the biological activity were used to determine the accurate scoring function for virtual screening. The r 2 values were 0.878, 0.696, 0.395, 0.276, 0.050, and 0.187 for DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF, respectively. According to the accurate DockScore, most of flavonoids and YC-1 derivatives had the higher binding affinities to HSP90 than controls and built the important hydrogen bond with the key residue ASP93. The structure-based de novo design by using Ludi program was performed to increase the binding affinity. Final thirteen potential compounds had higher binding affinity than the original ones. These candidates might guide drug design for novel HSP90 inhibitors in the future.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2008.4633952</doi><tpages>8</tpages></addata></record> |
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subjects | Artificial neural networks Compounds Correlation Crystals Inhibitors Joints Proteins |
title | A novel strategy for the structure-based drug design of heat shock protein 90 inhibitors |
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