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Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation
Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TN...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926523/ https://www.ncbi.nlm.nih.gov/pubmed/33671607 http://dx.doi.org/10.3390/biom11020329 |
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author | Halim, Sobia Ahsan Sikandari, Almas Gul Khan, Ajmal Wadood, Abdul Fatmi, Muhammad Qaiser Csuk, René Al-Harrasi, Ahmed |
author_facet | Halim, Sobia Ahsan Sikandari, Almas Gul Khan, Ajmal Wadood, Abdul Fatmi, Muhammad Qaiser Csuk, René Al-Harrasi, Ahmed |
author_sort | Halim, Sobia Ahsan |
collection | PubMed |
description | Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13–15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13–15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders. |
format | Online Article Text |
id | pubmed-7926523 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79265232021-03-04 Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation Halim, Sobia Ahsan Sikandari, Almas Gul Khan, Ajmal Wadood, Abdul Fatmi, Muhammad Qaiser Csuk, René Al-Harrasi, Ahmed Biomolecules Article Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13–15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13–15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders. MDPI 2021-02-22 /pmc/articles/PMC7926523/ /pubmed/33671607 http://dx.doi.org/10.3390/biom11020329 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Halim, Sobia Ahsan Sikandari, Almas Gul Khan, Ajmal Wadood, Abdul Fatmi, Muhammad Qaiser Csuk, René Al-Harrasi, Ahmed Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation |
title | Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation |
title_full | Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation |
title_fullStr | Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation |
title_full_unstemmed | Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation |
title_short | Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation |
title_sort | structure-based virtual screening of tumor necrosis factor-α inhibitors by cheminformatics approaches and bio-molecular simulation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926523/ https://www.ncbi.nlm.nih.gov/pubmed/33671607 http://dx.doi.org/10.3390/biom11020329 |
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