双氢青蒿素结合人PI3K-β亲和口袋并迫使P-loop MET783形成扁平构象:分子动力学研究

IF 3.1 Q2 TOXICOLOGY
Idowu Olaposi Omotuyi Prof , Oyekanmi Nash Prof , Samuel Damilohun Metibemu Dr. , G. Chiamaka Iwegbulam , Olusina M. Olatunji , Emmanuel Agbebi , C. Olufunke Falade
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引用次数: 0

摘要

青蒿素及其半合成衍生物不仅适用于疟疾,还适用于癌症、炎症和自身免疫性疾病。其炎症和免疫抑制靶点是PI3K/AKT通路。在当前的研究中,使用计算方法研究了PI3K抑制的结构和动力学方面。使用MMPBSA方法计算双氢青蒿素(DHA)与p110-PI3K-β的结合能,并与标准抑制剂(GD9)进行比较。还使用自适应采样协议和马尔可夫状态模型分析对复合物的动力学参数(Kon/Koff)进行了评估。p110-PI3K-β动力学和群落网络分析也按照常规分子动力学模拟进行。结果显示,DHA和GD9的结合能分别为−63.99±1.53和−74.14±3.47(Kj/mol)。DHA和GD9的Kon/Koff估计值分别为12.4和2.13(M−1)。轨迹分析表明,DHA选择性地分配到p110-PI3K-β亲和口袋中,迫使构象打开,并使催化口袋-M783保持平坦构象,同时迫使C2结构域周围发生大位移。总之,DHA是一种高亲和力(缓慢结合、缓慢解离)、平坦构象的p110-PI3K-β抑制剂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dihydroartemisinin binds human PI3K-β affinity pocket and forces flat conformation in P-loop MET783: A molecular dynamics study

Artemisinin and its semi-synthetic derivatives are not only indicated for malaria but also cancer, inflammatory and autoimmune diseases. Its inflammatory and immunosuppressive target is PI3K/AKT pathways. The structural and kinetic aspect of the PI3K inhibition was investigated in the current study using computational approaches. Binding energies of dihydroartemisinin (DHA) to p110-PI3K-β was computed using the MMPBSA method in comparison with the standard inhibitor (GD9). Kinetic parameter (Kon/Koff) was also evaluated for the complexes using adaptive sampling protocols and Markov state model analysis. p110-PI3K- β dynamics and community network analysis were also performed following conventional Molecular dynamics simulation. The results showed −63.99 ± 1.53 and −74.14 ± 3.47 (Kj/mol) binding energies for DHA and GD9 respectively. Kon/Koff estimates for DHA and GD9 are 12.4, and 2.13 (M−1) respectively. Analysis of the trajectories showed that DHA selectively partitions into p110-PI3K- β affinity pocket, forces open conformation, and kept catalytic pocket-M783 in a flat conformation whilst forcing large displacement around the C2-domain. In conclusion, DHA is a high affinity (slow-binding, slow-dissociating), flat-conformation p110-PI3K- β inhibitor.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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