Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Halim, Sobia Ahsan, Sikandari, Almas Gul, Khan, Ajmal, Wadood, Abdul, Fatmi, Muhammad Qaiser, Csuk, René, Al-Harrasi, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783659486034001920
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
work_keys_str_mv AT halimsobiaahsan structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation
AT sikandarialmasgul structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation
AT khanajmal structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation
AT wadoodabdul structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation
AT fatmimuhammadqaiser structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation
AT csukrene structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation
AT alharrasiahmed structurebasedvirtualscreeningoftumornecrosisfactorainhibitorsbycheminformaticsapproachesandbiomolecularsimulation