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Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors

This study provides a comprehensive computational procedure for the discovery of novel urea-based antineoplastic kinase inhibitors while focusing on diversification of both chemotype and selectivity pattern. It presents a systematic structural analysis of the different binding motifs of urea-based k...

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Autores principales: Dokla, Eman M., Mahmoud, Amr H., Elsayed, Mohamed S. A., El-Khatib, Ahmed H., Linscheid, Michael W., Abouzid, Khaled A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3502486/
https://www.ncbi.nlm.nih.gov/pubmed/23185312
http://dx.doi.org/10.1371/journal.pone.0049284
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author Dokla, Eman M.
Mahmoud, Amr H.
Elsayed, Mohamed S. A.
El-Khatib, Ahmed H.
Linscheid, Michael W.
Abouzid, Khaled A.
author_facet Dokla, Eman M.
Mahmoud, Amr H.
Elsayed, Mohamed S. A.
El-Khatib, Ahmed H.
Linscheid, Michael W.
Abouzid, Khaled A.
author_sort Dokla, Eman M.
collection PubMed
description This study provides a comprehensive computational procedure for the discovery of novel urea-based antineoplastic kinase inhibitors while focusing on diversification of both chemotype and selectivity pattern. It presents a systematic structural analysis of the different binding motifs of urea-based kinase inhibitors and the corresponding configurations of the kinase enzymes. The computational model depends on simultaneous application of two protocols. The first protocol applies multiple consecutive validated virtual screening filters including SMARTS, support vector-machine model (ROC = 0.98), Bayesian model (ROC = 0.86) and structure-based pharmacophore filters based on urea-based kinase inhibitors complexes retrieved from literature. This is followed by hits profiling against different extended electron distribution (XED) based field templates representing different kinase targets. The second protocol enables cancericidal activity verification by using the algorithm of feature trees (Ftrees) similarity searching against NCI database. Being a proof-of-concept study, this combined procedure was experimentally validated by its utilization in developing a novel series of urea-based derivatives of strong anticancer activity. This new series is based on 3-benzylbenzo[d]thiazol-2(3H)-one scaffold which has interesting chemical feasibility and wide diversification capability. Antineoplastic activity of this series was assayed in vitro against NCI 60 tumor-cell lines showing very strong inhibition of GI(50) as low as 0.9 uM. Additionally, its mechanism was unleashed using KINEX™ protein kinase microarray-based small molecule inhibitor profiling platform and cell cycle analysis showing a peculiar selectivity pattern against Zap70, c-src, Mink1, csk and MeKK2 kinases. Interestingly, it showed activity on syk kinase confirming the recent studies finding of the high activity of diphenyl urea containing compounds against this kinase. Allover, the new series, which is based on a new kinase scaffold with interesting chemical diversification capabilities, showed that it exhibits its “emergent” properties by perturbing multiple unexplored kinase pathways.
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spelling pubmed-35024862012-11-26 Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors Dokla, Eman M. Mahmoud, Amr H. Elsayed, Mohamed S. A. El-Khatib, Ahmed H. Linscheid, Michael W. Abouzid, Khaled A. PLoS One Research Article This study provides a comprehensive computational procedure for the discovery of novel urea-based antineoplastic kinase inhibitors while focusing on diversification of both chemotype and selectivity pattern. It presents a systematic structural analysis of the different binding motifs of urea-based kinase inhibitors and the corresponding configurations of the kinase enzymes. The computational model depends on simultaneous application of two protocols. The first protocol applies multiple consecutive validated virtual screening filters including SMARTS, support vector-machine model (ROC = 0.98), Bayesian model (ROC = 0.86) and structure-based pharmacophore filters based on urea-based kinase inhibitors complexes retrieved from literature. This is followed by hits profiling against different extended electron distribution (XED) based field templates representing different kinase targets. The second protocol enables cancericidal activity verification by using the algorithm of feature trees (Ftrees) similarity searching against NCI database. Being a proof-of-concept study, this combined procedure was experimentally validated by its utilization in developing a novel series of urea-based derivatives of strong anticancer activity. This new series is based on 3-benzylbenzo[d]thiazol-2(3H)-one scaffold which has interesting chemical feasibility and wide diversification capability. Antineoplastic activity of this series was assayed in vitro against NCI 60 tumor-cell lines showing very strong inhibition of GI(50) as low as 0.9 uM. Additionally, its mechanism was unleashed using KINEX™ protein kinase microarray-based small molecule inhibitor profiling platform and cell cycle analysis showing a peculiar selectivity pattern against Zap70, c-src, Mink1, csk and MeKK2 kinases. Interestingly, it showed activity on syk kinase confirming the recent studies finding of the high activity of diphenyl urea containing compounds against this kinase. Allover, the new series, which is based on a new kinase scaffold with interesting chemical diversification capabilities, showed that it exhibits its “emergent” properties by perturbing multiple unexplored kinase pathways. Public Library of Science 2012-11-20 /pmc/articles/PMC3502486/ /pubmed/23185312 http://dx.doi.org/10.1371/journal.pone.0049284 Text en © 2012 Dokla et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Dokla, Eman M.
Mahmoud, Amr H.
Elsayed, Mohamed S. A.
El-Khatib, Ahmed H.
Linscheid, Michael W.
Abouzid, Khaled A.
Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors
title Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors
title_full Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors
title_fullStr Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors
title_full_unstemmed Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors
title_short Applying Ligands Profiling Using Multiple Extended Electron Distribution Based Field Templates and Feature Trees Similarity Searching in the Discovery of New Generation of Urea-Based Antineoplastic Kinase Inhibitors
title_sort applying ligands profiling using multiple extended electron distribution based field templates and feature trees similarity searching in the discovery of new generation of urea-based antineoplastic kinase inhibitors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3502486/
https://www.ncbi.nlm.nih.gov/pubmed/23185312
http://dx.doi.org/10.1371/journal.pone.0049284
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