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Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach

The overexpression of matrix metalloproteinase-9 (MMP-9) is associated with tumor development and angiogenesis, and hence, it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP-9 targets, an in silico study was presented to investigate wh...

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Autores principales: Mathpal, Shalini, Sharma, Priyanka, Joshi, Tushar, Pande, Veena, Mahmud, Shafi, Jeong, Mi-Kyung, Obaidullah, Ahmad J., Chandra, Subhash, Kim, Bonglee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024349/
https://www.ncbi.nlm.nih.gov/pubmed/35463960
http://dx.doi.org/10.3389/fmolb.2022.857430
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author Mathpal, Shalini
Sharma, Priyanka
Joshi, Tushar
Pande, Veena
Mahmud, Shafi
Jeong, Mi-Kyung
Obaidullah, Ahmad J.
Chandra, Subhash
Kim, Bonglee
author_facet Mathpal, Shalini
Sharma, Priyanka
Joshi, Tushar
Pande, Veena
Mahmud, Shafi
Jeong, Mi-Kyung
Obaidullah, Ahmad J.
Chandra, Subhash
Kim, Bonglee
author_sort Mathpal, Shalini
collection PubMed
description The overexpression of matrix metalloproteinase-9 (MMP-9) is associated with tumor development and angiogenesis, and hence, it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP-9 targets, an in silico study was presented to investigate whether our compounds inhibit MMP-9 by binding to the catalytic domain, similar to their inhibitor or not. For that, in the initial stage, a deep-learning algorithm was used for the predictive modeling of the CHEMBL321 dataset of MMP-9 inhibitors. Several regression models were built and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model was utilized to screen the drug bank database containing 9,102 compounds to seek novel compounds as MMP-9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of the reference molecule. Furthermore, molecules having the highest docking scores were selected, and interaction mechanisms with respect to S1 pocket and catalytic zinc ion of these compounds were also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP-9, were considered preferentially for molecular dynamics studies (100 ns) and an MM-PBSA (last 30 ns) analysis. Based on the results, we proposed several novel compounds as potential candidates for MMP-9 inhibition and investigated their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP-9 inhibitors in the future.
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spelling pubmed-90243492022-04-23 Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach Mathpal, Shalini Sharma, Priyanka Joshi, Tushar Pande, Veena Mahmud, Shafi Jeong, Mi-Kyung Obaidullah, Ahmad J. Chandra, Subhash Kim, Bonglee Front Mol Biosci Molecular Biosciences The overexpression of matrix metalloproteinase-9 (MMP-9) is associated with tumor development and angiogenesis, and hence, it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP-9 targets, an in silico study was presented to investigate whether our compounds inhibit MMP-9 by binding to the catalytic domain, similar to their inhibitor or not. For that, in the initial stage, a deep-learning algorithm was used for the predictive modeling of the CHEMBL321 dataset of MMP-9 inhibitors. Several regression models were built and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model was utilized to screen the drug bank database containing 9,102 compounds to seek novel compounds as MMP-9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of the reference molecule. Furthermore, molecules having the highest docking scores were selected, and interaction mechanisms with respect to S1 pocket and catalytic zinc ion of these compounds were also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP-9, were considered preferentially for molecular dynamics studies (100 ns) and an MM-PBSA (last 30 ns) analysis. Based on the results, we proposed several novel compounds as potential candidates for MMP-9 inhibition and investigated their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP-9 inhibitors in the future. Frontiers Media S.A. 2022-03-31 /pmc/articles/PMC9024349/ /pubmed/35463960 http://dx.doi.org/10.3389/fmolb.2022.857430 Text en Copyright © 2022 Mathpal, Sharma, Joshi, Pande, Mahmud, Jeong, Obaidullah, Chandra and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Mathpal, Shalini
Sharma, Priyanka
Joshi, Tushar
Pande, Veena
Mahmud, Shafi
Jeong, Mi-Kyung
Obaidullah, Ahmad J.
Chandra, Subhash
Kim, Bonglee
Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach
title Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach
title_full Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach
title_fullStr Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach
title_full_unstemmed Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach
title_short Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach
title_sort identification of zinc-binding inhibitors of matrix metalloproteinase-9 to prevent cancer through deep learning and molecular dynamics simulation approach
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024349/
https://www.ncbi.nlm.nih.gov/pubmed/35463960
http://dx.doi.org/10.3389/fmolb.2022.857430
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