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Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations

BRD9 and TAF1(2) have been regarded as significant targets of drug design for clinically treating acute myeloid leukemia, malignancies, and inflammatory diseases. In this study, multiple short molecular dynamics simulations combined with the molecular mechanics generalized Born surface area method w...

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Autores principales: Wang, Lifei, Wang, Yan, Yu, Yingxia, Liu, Dong, Zhao, Juan, Zhang, Lulu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052767/
https://www.ncbi.nlm.nih.gov/pubmed/36985555
http://dx.doi.org/10.3390/molecules28062583
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author Wang, Lifei
Wang, Yan
Yu, Yingxia
Liu, Dong
Zhao, Juan
Zhang, Lulu
author_facet Wang, Lifei
Wang, Yan
Yu, Yingxia
Liu, Dong
Zhao, Juan
Zhang, Lulu
author_sort Wang, Lifei
collection PubMed
description BRD9 and TAF1(2) have been regarded as significant targets of drug design for clinically treating acute myeloid leukemia, malignancies, and inflammatory diseases. In this study, multiple short molecular dynamics simulations combined with the molecular mechanics generalized Born surface area method were employed to investigate the binding selectivity of three ligands, 67B, 67C, and 69G, to BRD9/TAF1(2) with IC(50) values of 230/59 nM, 1400/46 nM, and 160/410 nM, respectively. The computed binding free energies from the MM-GBSA method displayed good correlations with that provided by the experimental data. The results indicate that the enthalpic contributions played a critical factor in the selectivity recognition of inhibitors toward BRD9 and TAF1(2), indicating that 67B and 67C could more favorably bind to TAF1(2) than BRD9, while 69G had better selectivity toward BRD9 over TAF1(2). In addition, the residue-based free energy decomposition approach was adopted to calculate the inhibitor–residue interaction spectrum, and the results determined the gatekeeper (Y106 in BRD9 and Y1589 in TAF1(2)) and lipophilic shelf (G43, F44, and F45 in BRD9 and W1526, P1527, and F1528 in TAF1(2)), which could be identified as hotspots for designing efficient selective inhibitors toward BRD9 and TAF1(2). This work is also expected to provide significant theoretical guidance and insightful molecular mechanisms for the rational designs of efficient selective inhibitors targeting BRD9 and TAF1(2).
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spelling pubmed-100527672023-03-30 Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations Wang, Lifei Wang, Yan Yu, Yingxia Liu, Dong Zhao, Juan Zhang, Lulu Molecules Article BRD9 and TAF1(2) have been regarded as significant targets of drug design for clinically treating acute myeloid leukemia, malignancies, and inflammatory diseases. In this study, multiple short molecular dynamics simulations combined with the molecular mechanics generalized Born surface area method were employed to investigate the binding selectivity of three ligands, 67B, 67C, and 69G, to BRD9/TAF1(2) with IC(50) values of 230/59 nM, 1400/46 nM, and 160/410 nM, respectively. The computed binding free energies from the MM-GBSA method displayed good correlations with that provided by the experimental data. The results indicate that the enthalpic contributions played a critical factor in the selectivity recognition of inhibitors toward BRD9 and TAF1(2), indicating that 67B and 67C could more favorably bind to TAF1(2) than BRD9, while 69G had better selectivity toward BRD9 over TAF1(2). In addition, the residue-based free energy decomposition approach was adopted to calculate the inhibitor–residue interaction spectrum, and the results determined the gatekeeper (Y106 in BRD9 and Y1589 in TAF1(2)) and lipophilic shelf (G43, F44, and F45 in BRD9 and W1526, P1527, and F1528 in TAF1(2)), which could be identified as hotspots for designing efficient selective inhibitors toward BRD9 and TAF1(2). This work is also expected to provide significant theoretical guidance and insightful molecular mechanisms for the rational designs of efficient selective inhibitors targeting BRD9 and TAF1(2). MDPI 2023-03-12 /pmc/articles/PMC10052767/ /pubmed/36985555 http://dx.doi.org/10.3390/molecules28062583 Text en © 2023 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
Wang, Lifei
Wang, Yan
Yu, Yingxia
Liu, Dong
Zhao, Juan
Zhang, Lulu
Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations
title Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations
title_full Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations
title_fullStr Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations
title_full_unstemmed Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations
title_short Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations
title_sort deciphering selectivity mechanism of brd9 and taf1(2) toward inhibitors based on multiple short molecular dynamics simulations and mm-gbsa calculations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052767/
https://www.ncbi.nlm.nih.gov/pubmed/36985555
http://dx.doi.org/10.3390/molecules28062583
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