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Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach

Mycobacterium tuberculosis (Mtb) is a deadly tuberculosis (TB)-causing pathogen. The proteasome is vital to the survival of Mtb and is therefore validated as a potential target for anti-TB therapy. Mtb resistance to existing antibacterial agents has enhanced drastically, becoming a worldwide health...

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Autores principales: Almeleebia, Tahani M., Shahrani, Mesfer Al, Alshahrani, Mohammad Y., Ahmad, Irfan, Alkahtani, Abdullah M., Alam, Md Jahoor, Kausar, Mohd Adnan, Saeed, Amir, Saeed, Mohd, Iram, Sana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074214/
https://www.ncbi.nlm.nih.gov/pubmed/33923734
http://dx.doi.org/10.3390/molecules26082326
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author Almeleebia, Tahani M.
Shahrani, Mesfer Al
Alshahrani, Mohammad Y.
Ahmad, Irfan
Alkahtani, Abdullah M.
Alam, Md Jahoor
Kausar, Mohd Adnan
Saeed, Amir
Saeed, Mohd
Iram, Sana
author_facet Almeleebia, Tahani M.
Shahrani, Mesfer Al
Alshahrani, Mohammad Y.
Ahmad, Irfan
Alkahtani, Abdullah M.
Alam, Md Jahoor
Kausar, Mohd Adnan
Saeed, Amir
Saeed, Mohd
Iram, Sana
author_sort Almeleebia, Tahani M.
collection PubMed
description Mycobacterium tuberculosis (Mtb) is a deadly tuberculosis (TB)-causing pathogen. The proteasome is vital to the survival of Mtb and is therefore validated as a potential target for anti-TB therapy. Mtb resistance to existing antibacterial agents has enhanced drastically, becoming a worldwide health issue. Therefore, new potential therapeutic agents need to be developed that can overcome the complications of TB. With this purpose, in the present study, 224,205 natural compounds from the ZINC database have been screened against the catalytic site of Mtb proteasome by the computational approach. The best scoring hits, ZINC3875469, ZINC4076131, and ZINC1883067, demonstrated robust interaction with Mtb proteasome with binding energy values of −7.19, −7.95, and −7.21 kcal/mol for the monomer (K-chain) and −8.05, −9.10, and −7.07 kcal/mol for the dimer (both K and L chains) of the beta subunit, which is relatively higher than that of reference compound HT1171 (−5.83 kcal/mol (monomer) and −5.97 kcal/mol (dimer)). In-depth molecular docking of top-scoring compounds with Mtb proteasome reveals that amino acid residues Thr1, Arg19, Ser20, Thr21, Gln22, Gly23, Asn24, Lys33, Gly47, Asp124, Ala126, Trp129, and Ala180 are crucial in binding. Furthermore, a molecular dynamics study showed steady-state interaction of hit compounds with Mtb proteasome. Computational prediction of physicochemical property assessment showed that these hits are non-toxic and possess good drug-likeness properties. This study proposed that these compounds could be utilized as potential inhibitors of Mtb proteasome to combat TB infection. However, there is a need for further bench work experiments for their validation as inhibitors of Mtb proteasome.
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spelling pubmed-80742142021-04-27 Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach Almeleebia, Tahani M. Shahrani, Mesfer Al Alshahrani, Mohammad Y. Ahmad, Irfan Alkahtani, Abdullah M. Alam, Md Jahoor Kausar, Mohd Adnan Saeed, Amir Saeed, Mohd Iram, Sana Molecules Article Mycobacterium tuberculosis (Mtb) is a deadly tuberculosis (TB)-causing pathogen. The proteasome is vital to the survival of Mtb and is therefore validated as a potential target for anti-TB therapy. Mtb resistance to existing antibacterial agents has enhanced drastically, becoming a worldwide health issue. Therefore, new potential therapeutic agents need to be developed that can overcome the complications of TB. With this purpose, in the present study, 224,205 natural compounds from the ZINC database have been screened against the catalytic site of Mtb proteasome by the computational approach. The best scoring hits, ZINC3875469, ZINC4076131, and ZINC1883067, demonstrated robust interaction with Mtb proteasome with binding energy values of −7.19, −7.95, and −7.21 kcal/mol for the monomer (K-chain) and −8.05, −9.10, and −7.07 kcal/mol for the dimer (both K and L chains) of the beta subunit, which is relatively higher than that of reference compound HT1171 (−5.83 kcal/mol (monomer) and −5.97 kcal/mol (dimer)). In-depth molecular docking of top-scoring compounds with Mtb proteasome reveals that amino acid residues Thr1, Arg19, Ser20, Thr21, Gln22, Gly23, Asn24, Lys33, Gly47, Asp124, Ala126, Trp129, and Ala180 are crucial in binding. Furthermore, a molecular dynamics study showed steady-state interaction of hit compounds with Mtb proteasome. Computational prediction of physicochemical property assessment showed that these hits are non-toxic and possess good drug-likeness properties. This study proposed that these compounds could be utilized as potential inhibitors of Mtb proteasome to combat TB infection. However, there is a need for further bench work experiments for their validation as inhibitors of Mtb proteasome. MDPI 2021-04-16 /pmc/articles/PMC8074214/ /pubmed/33923734 http://dx.doi.org/10.3390/molecules26082326 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Almeleebia, Tahani M.
Shahrani, Mesfer Al
Alshahrani, Mohammad Y.
Ahmad, Irfan
Alkahtani, Abdullah M.
Alam, Md Jahoor
Kausar, Mohd Adnan
Saeed, Amir
Saeed, Mohd
Iram, Sana
Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
title Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
title_full Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
title_fullStr Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
title_full_unstemmed Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
title_short Identification of New Mycobacterium tuberculosis Proteasome Inhibitors Using a Knowledge-Based Computational Screening Approach
title_sort identification of new mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074214/
https://www.ncbi.nlm.nih.gov/pubmed/33923734
http://dx.doi.org/10.3390/molecules26082326
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