Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics

Search for new antiviral medications has surged in the past two years due to the COVID-19 crisis. Toll-like receptor 7 (TLR7) is among one of the most important TLR proteins of innate immunity that is responsible for broad antiviral response and immune system control. TLR7 agonists, as both vaccine...

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Autores principales:	Ardavan Abiri, Masoud Rezaei, Mohammad Hossein Zeighami, Younes Vaezpour, Leili Dehghan, Maedeh KhorramGhahfarokhi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	TLR7 Antiviral Statistical learning Drug design QSAR Molecular docking Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/d4b8ad598b8a4ec79183a44ac3cc0543
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spelling	oai:doaj.org-article:d4b8ad598b8a4ec79183a44ac3cc05432021-11-18T04:50:26ZDiscovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics2352-914810.1016/j.imu.2021.100787https://doaj.org/article/d4b8ad598b8a4ec79183a44ac3cc05432021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352914821002586https://doaj.org/toc/2352-9148Search for new antiviral medications has surged in the past two years due to the COVID-19 crisis. Toll-like receptor 7 (TLR7) is among one of the most important TLR proteins of innate immunity that is responsible for broad antiviral response and immune system control. TLR7 agonists, as both vaccine adjuvants and immune response modulators, are among the top drug candidates for not only our contemporary viral pandemic but also other diseases. The agonists of TLR7 have been utilized as vaccine adjuvants and antiviral agents. In this study, we hybridized a statistical learning-based QSAR model with molecular docking and molecular dynamics simulation to extract new antiviral drugs by drug repurposing of the DrugBank database. First, we manually curated a dataset consisting of TLR7 agonists. The molecular descriptors of these compounds were extracted, and feature engineering was done to restrict the number of features to 45. We applied a statistically inspired modification of the partial least squares (SIMPLS) method to build our QSAR model. In the next stage, the DrugBank database was virtually screened structurally using molecular docking, and the top compounds for the guanosine binding site of TLR were identified. The result of molecular docking was again screened by the ligand-based approach of QSAR to eliminate compounds that do not display strong EC50 values by the previously trained model. We then subjected the final results to molecular dynamics simulation and compared our compounds with imiquimod (an FDA-approved TLR7 agonist) and compound 1 (the most active compound against TLR7 in vitro, EC50 = 0.2 nM). Our results evidently demonstrate that cephalosporins and nucleotide analogues (especially acyclic nucleotide analogues such as adefovir and cidofovir) are computationally potent agonists of TLR7. We finally reviewed some publications about cephalosporins that, just like pieces of a puzzle, completed our conclusion.Ardavan AbiriMasoud RezaeiMohammad Hossein ZeighamiYounes VaezpourLeili DehghanMaedeh KhorramGhahfarokhiElsevierarticleTLR7AntiviralStatistical learningDrug designQSARMolecular dockingComputer applications to medicine. Medical informaticsR858-859.7ENInformatics in Medicine Unlocked, Vol 27, Iss , Pp 100787- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	TLR7 Antiviral Statistical learning Drug design QSAR Molecular docking Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	TLR7 Antiviral Statistical learning Drug design QSAR Molecular docking Computer applications to medicine. Medical informatics R858-859.7 Ardavan Abiri Masoud Rezaei Mohammad Hossein Zeighami Younes Vaezpour Leili Dehghan Maedeh KhorramGhahfarokhi Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics
description	Search for new antiviral medications has surged in the past two years due to the COVID-19 crisis. Toll-like receptor 7 (TLR7) is among one of the most important TLR proteins of innate immunity that is responsible for broad antiviral response and immune system control. TLR7 agonists, as both vaccine adjuvants and immune response modulators, are among the top drug candidates for not only our contemporary viral pandemic but also other diseases. The agonists of TLR7 have been utilized as vaccine adjuvants and antiviral agents. In this study, we hybridized a statistical learning-based QSAR model with molecular docking and molecular dynamics simulation to extract new antiviral drugs by drug repurposing of the DrugBank database. First, we manually curated a dataset consisting of TLR7 agonists. The molecular descriptors of these compounds were extracted, and feature engineering was done to restrict the number of features to 45. We applied a statistically inspired modification of the partial least squares (SIMPLS) method to build our QSAR model. In the next stage, the DrugBank database was virtually screened structurally using molecular docking, and the top compounds for the guanosine binding site of TLR were identified. The result of molecular docking was again screened by the ligand-based approach of QSAR to eliminate compounds that do not display strong EC50 values by the previously trained model. We then subjected the final results to molecular dynamics simulation and compared our compounds with imiquimod (an FDA-approved TLR7 agonist) and compound 1 (the most active compound against TLR7 in vitro, EC50 = 0.2 nM). Our results evidently demonstrate that cephalosporins and nucleotide analogues (especially acyclic nucleotide analogues such as adefovir and cidofovir) are computationally potent agonists of TLR7. We finally reviewed some publications about cephalosporins that, just like pieces of a puzzle, completed our conclusion.
format	article
author	Ardavan Abiri Masoud Rezaei Mohammad Hossein Zeighami Younes Vaezpour Leili Dehghan Maedeh KhorramGhahfarokhi
author_facet	Ardavan Abiri Masoud Rezaei Mohammad Hossein Zeighami Younes Vaezpour Leili Dehghan Maedeh KhorramGhahfarokhi
author_sort	Ardavan Abiri
title	Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics
title_short	Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics
title_full	Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics
title_fullStr	Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics
title_full_unstemmed	Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics
title_sort	discovery of new tlr7 agonists by a combination of statistical learning-based qsar, virtual screening, and molecular dynamics
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d4b8ad598b8a4ec79183a44ac3cc0543
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Discovery of new TLR7 agonists by a combination of statistical learning-based QSAR, virtual screening, and molecular dynamics

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