Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models
Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best mode...
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Veröffentlicht in: | European journal of medicinal chemistry 2009-09, Vol.44 (9), p.3584-3590 |
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container_title | European journal of medicinal chemistry |
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creator | Boppana, Kiran Dubey, P.K. Jagarlapudi, Sarma A.R.P. Vadivelan, S. Rambabu, G. |
description | Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best model, Hypo2 consists of three pharmacophore features, i.e., one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic. The Hypo2 model was used to screen an in-house database of 80,000 molecules and have resulted in 5500 compounds. Docking studies were performed, subsequently, on the cluster representatives of 530 hits from 5500 compounds. Based on the structural novelty and selectivity index, we have suggested 15 selective MAO-B inhibitors for further synthesis and pharmacological screening.
[Display omitted] Hypo2 quantitative pharmacophore model of selective MAO B inhibitor which shows good prediction of training and test set compounds was compared to other nine hypotheses. |
doi_str_mv | 10.1016/j.ejmech.2009.02.031 |
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[Display omitted] Hypo2 quantitative pharmacophore model of selective MAO B inhibitor which shows good prediction of training and test set compounds was compared to other nine hypotheses.</description><identifier>ISSN: 0223-5234</identifier><identifier>EISSN: 1768-3254</identifier><identifier>DOI: 10.1016/j.ejmech.2009.02.031</identifier><identifier>PMID: 19321235</identifier><identifier>CODEN: EJMCA5</identifier><language>eng</language><publisher>Kidlington: Elsevier Masson SAS</publisher><subject>Biological and medical sciences ; Catalyst ; Catalytic Domain ; Docking ; Drug Design ; Glide ; Humans ; MAO-B ; Medical sciences ; Miscellaneous ; Models, Molecular ; Molecular Structure ; Monoamine Oxidase - chemistry ; Monoamine Oxidase - metabolism ; Monoamine Oxidase Inhibitors - chemistry ; Monoamine Oxidase Inhibitors - metabolism ; Neuropharmacology ; Pharmacology. Drug treatments ; Pharmacophore ; Protein Binding ; Structure-Activity Relationship</subject><ispartof>European journal of medicinal chemistry, 2009-09, Vol.44 (9), p.3584-3590</ispartof><rights>2009 Elsevier Masson SAS</rights><rights>2009 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c390t-a8e158fb65683e556bca601dda774cd5cd7d2a0946b2f57b42d21292f463aec3</citedby><cites>FETCH-LOGICAL-c390t-a8e158fb65683e556bca601dda774cd5cd7d2a0946b2f57b42d21292f463aec3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejmech.2009.02.031$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21777563$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19321235$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Boppana, Kiran</creatorcontrib><creatorcontrib>Dubey, P.K.</creatorcontrib><creatorcontrib>Jagarlapudi, Sarma A.R.P.</creatorcontrib><creatorcontrib>Vadivelan, S.</creatorcontrib><creatorcontrib>Rambabu, G.</creatorcontrib><title>Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models</title><title>European journal of medicinal chemistry</title><addtitle>Eur J Med Chem</addtitle><description>Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best model, Hypo2 consists of three pharmacophore features, i.e., one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic. The Hypo2 model was used to screen an in-house database of 80,000 molecules and have resulted in 5500 compounds. Docking studies were performed, subsequently, on the cluster representatives of 530 hits from 5500 compounds. Based on the structural novelty and selectivity index, we have suggested 15 selective MAO-B inhibitors for further synthesis and pharmacological screening.
[Display omitted] Hypo2 quantitative pharmacophore model of selective MAO B inhibitor which shows good prediction of training and test set compounds was compared to other nine hypotheses.</description><subject>Biological and medical sciences</subject><subject>Catalyst</subject><subject>Catalytic Domain</subject><subject>Docking</subject><subject>Drug Design</subject><subject>Glide</subject><subject>Humans</subject><subject>MAO-B</subject><subject>Medical sciences</subject><subject>Miscellaneous</subject><subject>Models, Molecular</subject><subject>Molecular Structure</subject><subject>Monoamine Oxidase - chemistry</subject><subject>Monoamine Oxidase - metabolism</subject><subject>Monoamine Oxidase Inhibitors - chemistry</subject><subject>Monoamine Oxidase Inhibitors - metabolism</subject><subject>Neuropharmacology</subject><subject>Pharmacology. Drug treatments</subject><subject>Pharmacophore</subject><subject>Protein Binding</subject><subject>Structure-Activity Relationship</subject><issn>0223-5234</issn><issn>1768-3254</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1v1DAURS0EokPhHyDkDewS_BHbyQapVFAQRd10bzn2S8ejxB78kqlY8N_JaAbYsXqbc6_uO4S85qzmjOv3uxp2E_htLRjraiZqJvkTsuFGt5UUqnlKNkwIWSkhmwvyAnHHGFOasefkgndScCHVhvz6lvLjCOEBaO8QAo0B0hyH6N0cc6J5oN-v7qqPFGEEP8cD0Ji2sY9zLkgXjOmB7reuTM7n_TYXoC4FinNZ_LyUP6WHWObFjRR9AUjHzJQDjPiSPBvciPDqfC_J_edP99dfqtu7m6_XV7eVlx2bK9cCV-3Qa6VbCUrp3jvNeAjOmMYH5YMJwrGu0b0YlOkbEdb3OjE0Wjrw8pK8O9XuS_6xAM52iuhhHF2CvKDVpulaYcwKNifQl4xYYLD7EidXflrO7NG63dmTdXu0bpmwq_U19ubcv_QThH-hs-YVeHsGHHo3DsUlH_EvJ7gxRmm5ch9O3OoGDhGKRR8heQixrPZtyPH_S34DPh6kpg</recordid><startdate>20090901</startdate><enddate>20090901</enddate><creator>Boppana, Kiran</creator><creator>Dubey, P.K.</creator><creator>Jagarlapudi, Sarma A.R.P.</creator><creator>Vadivelan, S.</creator><creator>Rambabu, G.</creator><general>Elsevier Masson SAS</general><general>Elsevier</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>7X8</scope></search><sort><creationdate>20090901</creationdate><title>Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models</title><author>Boppana, Kiran ; Dubey, P.K. ; Jagarlapudi, Sarma A.R.P. ; Vadivelan, S. ; Rambabu, G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-a8e158fb65683e556bca601dda774cd5cd7d2a0946b2f57b42d21292f463aec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Biological and medical sciences</topic><topic>Catalyst</topic><topic>Catalytic Domain</topic><topic>Docking</topic><topic>Drug Design</topic><topic>Glide</topic><topic>Humans</topic><topic>MAO-B</topic><topic>Medical sciences</topic><topic>Miscellaneous</topic><topic>Models, Molecular</topic><topic>Molecular Structure</topic><topic>Monoamine Oxidase - chemistry</topic><topic>Monoamine Oxidase - metabolism</topic><topic>Monoamine Oxidase Inhibitors - chemistry</topic><topic>Monoamine Oxidase Inhibitors - metabolism</topic><topic>Neuropharmacology</topic><topic>Pharmacology. Drug treatments</topic><topic>Pharmacophore</topic><topic>Protein Binding</topic><topic>Structure-Activity Relationship</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boppana, Kiran</creatorcontrib><creatorcontrib>Dubey, P.K.</creatorcontrib><creatorcontrib>Jagarlapudi, Sarma A.R.P.</creatorcontrib><creatorcontrib>Vadivelan, S.</creatorcontrib><creatorcontrib>Rambabu, G.</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>MEDLINE - Academic</collection><jtitle>European journal of medicinal chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boppana, Kiran</au><au>Dubey, P.K.</au><au>Jagarlapudi, Sarma A.R.P.</au><au>Vadivelan, S.</au><au>Rambabu, G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models</atitle><jtitle>European journal of medicinal chemistry</jtitle><addtitle>Eur J Med Chem</addtitle><date>2009-09-01</date><risdate>2009</risdate><volume>44</volume><issue>9</issue><spage>3584</spage><epage>3590</epage><pages>3584-3590</pages><issn>0223-5234</issn><eissn>1768-3254</eissn><coden>EJMCA5</coden><abstract>Monoamine Oxidase B interaction with known ligands was investigated using combined pharmacophore and structure based modeling approach. The docking results suggested that the pharmacophore and docking models are in good agreement and are used to identify the selective MAO-B inhibitors. The best model, Hypo2 consists of three pharmacophore features, i.e., one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic. The Hypo2 model was used to screen an in-house database of 80,000 molecules and have resulted in 5500 compounds. Docking studies were performed, subsequently, on the cluster representatives of 530 hits from 5500 compounds. Based on the structural novelty and selectivity index, we have suggested 15 selective MAO-B inhibitors for further synthesis and pharmacological screening.
[Display omitted] Hypo2 quantitative pharmacophore model of selective MAO B inhibitor which shows good prediction of training and test set compounds was compared to other nine hypotheses.</abstract><cop>Kidlington</cop><pub>Elsevier Masson SAS</pub><pmid>19321235</pmid><doi>10.1016/j.ejmech.2009.02.031</doi><tpages>7</tpages></addata></record> |
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subjects | Biological and medical sciences Catalyst Catalytic Domain Docking Drug Design Glide Humans MAO-B Medical sciences Miscellaneous Models, Molecular Molecular Structure Monoamine Oxidase - chemistry Monoamine Oxidase - metabolism Monoamine Oxidase Inhibitors - chemistry Monoamine Oxidase Inhibitors - metabolism Neuropharmacology Pharmacology. Drug treatments Pharmacophore Protein Binding Structure-Activity Relationship |
title | Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models |
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