Drogalin Artem, Monteiro Luís S, Alves Maria José, Castro Tarsila G
Chemistry Centre, School of Sciences, University of Minho, Braga, Portugal.
CEB - Centre of Biological Engineering, University of Minho, Braga, Portugal.
J Biomol Struct Dyn. 2024 Mar;42(5):2714-2725. doi: 10.1080/07391102.2023.2209184. Epub 2023 May 9.
The search for Golgi α-mannosidase II (GMII) potent and specific inhibitors has been a focus of many studies for the past three decades since this enzyme is a key target for cancer treatment. α-Mannosidases, such as those from or Jack bean, have been used as functional models of the human Golgi α-mannosidase II (hGMII) because mammalian mannosidases are difficult to purify and characterize experimentally. Meanwhile, computational studies have been seen as privileged tools able to explore assertive solutions to specific enzymes, providing molecular details of these macromolecules, their protonation states and their interactions. Thus, modelling techniques can successfully predict hGMII 3D structure with high confidence, speeding up the development of new hits. In this study, Golgi mannosidase II (dGMII) and a novel human model, developed and equilibrated molecular dynamics simulations, were both opposed for docking. Our findings highlight that the design of novel inhibitors should be carried out considering the human model's characteristics and the enzyme operating pH. A reliable model is evidenced, showing a good correlation between K/IC experimental data and theoretical Δ estimations in GMII, opening the possibility of optimizing the rational drug design of new derivatives.Communicated by Ramaswamy H. Sarma.
在过去三十年里,寻找高尔基体α-甘露糖苷酶II(GMII)的强效和特异性抑制剂一直是众多研究的重点,因为这种酶是癌症治疗的关键靶点。α-甘露糖苷酶,如来自[具体来源未提及]或刀豆的那些,已被用作人类高尔基体α-甘露糖苷酶II(hGMII)的功能模型,因为哺乳动物的甘露糖苷酶很难通过实验进行纯化和表征。与此同时,计算研究被视为能够探索针对特定酶的可靠解决方案的特权工具,提供这些大分子的分子细节、它们的质子化状态及其相互作用。因此,建模技术可以成功地以高置信度预测hGMII的三维结构,加速新命中物的开发。在本研究中,将[具体物种未提及]的高尔基体甘露糖苷酶II(dGMII)和一个新开发并经过平衡分子动力学模拟的新型人类模型用于对接。我们的研究结果强调,新型抑制剂的设计应考虑人类模型的特征和酶的工作pH值。一个可靠的模型得到了验证,显示出GMII中K/IC实验数据与理论Δ估计值之间具有良好的相关性,为优化新衍生物的合理药物设计开辟了可能性。由拉马斯瓦米·H·萨尔马传达。