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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MDPI AG
2023-03-01
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author | Lifei Wang Yan Wang Yingxia Yu Dong Liu Juan Zhao Lulu Zhang |
author_facet | Lifei Wang Yan Wang Yingxia Yu Dong Liu Juan Zhao Lulu Zhang |
author_sort | Lifei Wang |
collection | DOAJ |
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<sub>50</sub> 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 | doaj.art-63089118afaa4a5f96c7b465441ce7bf2023-11-17T12:52:21ZengMDPI AGMolecules1420-30492023-03-01286258310.3390/molecules28062583Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA CalculationsLifei Wang0Yan Wang1Yingxia Yu2Dong Liu3Juan Zhao4Lulu Zhang5School of Science, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Science, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Science, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Science, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Science, Shandong Jiaotong University, Jinan 250357, ChinaSchool of Science, Shandong Jiaotong University, Jinan 250357, ChinaBRD9 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<sub>50</sub> 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).https://www.mdpi.com/1420-3049/28/6/2583BRD9TAF1(2)MSMDbinding selectivityfree energy landscapes |
spellingShingle | Lifei Wang Yan Wang Yingxia Yu Dong Liu Juan Zhao Lulu Zhang Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations Molecules BRD9 TAF1(2) MSMD binding selectivity free energy landscapes |
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 | BRD9 TAF1(2) MSMD binding selectivity free energy landscapes |
url | https://www.mdpi.com/1420-3049/28/6/2583 |
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