Perspective of structural flexibility on selective inhibition towards CYP1B1 over CYP1A1 by α-naphthoflavone analogs

Wang, Ying; Hu, Baichun; Zhang, Yupeng; Wang, Dong; Luo, Zhaohu; Wang, Jian; Zhang, Fengjiao

doi:10.1039/d1cp02541d

Cited by 7 publications

(18 citation statements)

References 35 publications

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“…From the literature, it is evident that PHE 231, PHE 268, and PHE 134 are crucial active residues involved in the development of pi–pi stacking interactions in CYP1B1 inhibition. 58 However, PHE 224 and 123 play an important role in selective inhibition of CYP1A1 by forming crucial pi–pi stacking interactions. 58 By considering this crucial interaction and its binding affinity scores, the selectivity of the identified hits was judged against 1B1 over 1A1.…”

Section: Resultsmentioning

confidence: 99%

“… 58 However, PHE 224 and 123 play an important role in selective inhibition of CYP1A1 by forming crucial pi–pi stacking interactions. 58 By considering this crucial interaction and its binding affinity scores, the selectivity of the identified hits was judged against 1B1 over 1A1.…”

Section: Resultsmentioning

confidence: 99%

“…As illustrated in Figure B, three pi–pi stacking interactions were observed between the naphthalene moiety of ANF and active site residues of PHE 231 and PHE 268, which are crucial for binding. From the literature, it is evident that PHE 231, PHE 268, and PHE 134 are crucial active residues involved in the development of pi–pi stacking interactions in CYP1B1 inhibition . However, PHE 224 and 123 play an important role in selective inhibition of CYP1A1 by forming crucial pi–pi stacking interactions .…”

Section: Resultsmentioning

confidence: 99%

“…58 However, PHE 224 and 123 play an important role in selective inhibition of CYP1A1 by forming crucial pi−pi stacking interactions. 58 By considering this crucial interaction and its binding affinity scores, the selectivity of the identified hits was judged against 1B1 over 1A1. Initially, all of the 660 hits were docked in the active site of CYP1B1, and their interaction profiles and docking scores were analyzed.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

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Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors

et al. 2022

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Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC 50 range of 0.07–3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: ■ Materials and Methodsmentioning

confidence: 99%

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Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors

et al. 2022

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“…Although the sequence similarity of CYP1A1 and CYP1A2 to CYP1B1 is only 38% and 37%, 13 their amino acids at the active site are closely similar. 14 This feature could be explained via ANF, as a co-crystalline ligand of three proteins. In recent years, some tumors have been reported to be induced by the enhanced expression of CYP1A1.…”

Section: Introductionmentioning

confidence: 99%

Design, synthesis and biological evaluation of highly potent and selective CYP1B1 inhibitors

Zhang

Wang

et al. 2023

New J. Chem.

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Uncovering the selectivity mechanism of phosphodiesterase 7A/8A inhibitors through computational studies

Wang,

Wang

et al. 2024

Phys. Chem. Chem. Phys.

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Perspective of structural flexibility on selective inhibition towards CYP1B1 over CYP1A1 by α-naphthoflavone analogs

Abstract: The structural flexibility of protein domains mainly orchestrated the sustainability of crucial π–π stacking interactions with the key phenylalanine residues of CYP1A1 and CYP1B1, thereby determining the inhibitory selectivity towards CYP1B1.

Cited by 7 publications

References 35 publications

Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors

Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors

Design, synthesis and biological evaluation of highly potent and selective CYP1B1 inhibitors

Uncovering the selectivity mechanism of phosphodiesterase 7A/8A inhibitors through computational studies

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