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通过 ABCB1 转运蛋白逆转多药耐药的超自然抑制剂:数据库挖掘、脂质介导的分子动力学和药代动力学研究。

SuperNatural inhibitors to reverse multidrug resistance emerged by ABCB1 transporter: Database mining, lipid-mediated molecular dynamics, and pharmacokinetics study.

机构信息

Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.

School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.

出版信息

PLoS One. 2023 Jul 26;18(7):e0288919. doi: 10.1371/journal.pone.0288919. eCollection 2023.

DOI:10.1371/journal.pone.0288919
PMID:37494356
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10370898/
Abstract

An effective approach to reverse multidrug resistance (MDR) is P-glycoprotein (P-gp, ABCB1) transport inhibition. To identify such molecular regulators, the SuperNatural II database, which comprises > 326,000 compounds, was virtually screened for ABCB1 transporter inhibitors. The Lipinski rule was utilized to initially screen the SuperNatural II database, identifying 128,126 compounds. Those natural compounds were docked against the ABCB1 transporter, and those with docking scores less than zosuquidar (ZQU) inhibitor were subjected to molecular dynamics (MD) simulations. Based on MM-GBA binding energy (ΔGbinding) estimations, UMHSN00009999 and UMHSN00097206 demonstrated ΔGbinding values of -68.3 and -64.1 kcal/mol, respectively, compared to ZQU with a ΔGbinding value of -49.8 kcal/mol. For an investigation of stability, structural and energetic analyses for UMHSN00009999- and UMHSN00097206-ABCB1 complexes were performed and proved the high steadiness of these complexes throughout 100 ns MD simulations. Pharmacokinetic properties of the identified compounds were also predicted. To mimic the physiological conditions, MD simulations in POPC membrane surroundings were applied to the UMHSN00009999- and UMHSN00097206-ABCB1 complexes. These results demonstrated that UMHSN00009999 and UMHSN00097206 are promising ABCB1 inhibitors for reversing MDR in cancer and warrant additional in-vitro/in-vivo studies.

摘要

一种有效的逆转多药耐药(MDR)的方法是抑制 P-糖蛋白(P-gp,ABCB1)转运。为了鉴定这种分子调节剂,使用了包含>326000种化合物的 SuperNatural II 数据库,对 ABCB1 转运体抑制剂进行了虚拟筛选。利用 Lipinski 规则对 SuperNatural II 数据库进行了初步筛选,确定了 128126 种化合物。将这些天然化合物对接至 ABCB1 转运体,对接得分低于 zosuquidar(ZQU)抑制剂的化合物则进行分子动力学(MD)模拟。基于 MM-GBA 结合能(ΔGbinding)估算,UMHSN00009999 和 UMHSN00097206 的 ΔGbinding 值分别为-68.3 和-64.1 kcal/mol,而 ZQU 的 ΔGbinding 值为-49.8 kcal/mol。为了研究稳定性,对 UMHSN00009999 和 UMHSN00097206-ABCB1 复合物进行了结构和能量分析,证明这些复合物在 100 ns MD 模拟过程中非常稳定。还预测了鉴定化合物的药代动力学性质。为了模拟生理条件,在 POPC 膜环境中对 UMHSN00009999 和 UMHSN00097206-ABCB1 复合物进行了 MD 模拟。结果表明,UMHSN00009999 和 UMHSN00097206 是有希望的逆转癌症 MDR 的 ABCB1 抑制剂,值得进一步进行体外/体内研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/13f444abb445/pone.0288919.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/c94d18e8e150/pone.0288919.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/89d8045c1d09/pone.0288919.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/b74cc674a815/pone.0288919.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/2c668403a52e/pone.0288919.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/c69e42f1e77b/pone.0288919.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/bba4adf25522/pone.0288919.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/13f444abb445/pone.0288919.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/c94d18e8e150/pone.0288919.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/1a2ad90847d8/pone.0288919.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/174dadfacb5c/pone.0288919.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/89d8045c1d09/pone.0288919.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/b74cc674a815/pone.0288919.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/2c668403a52e/pone.0288919.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/c69e42f1e77b/pone.0288919.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/bba4adf25522/pone.0288919.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e03/10370898/13f444abb445/pone.0288919.g009.jpg

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