通过基于计算机的药物设计挖掘有益心脏和利尿的生物活性化合物的抗糖尿病潜力

IF 2.6 4区 生物学 Q2 BIOLOGY
Nilufer Ercin , Nail Besli , Ulkan Kilic
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引用次数: 0

摘要

现代医学的先驱阿维森纳建议用利尿疗法治疗糖尿病。与阿维森纳的方法一样,目前的医学也经常开具具有利尿和降血糖作用的口服抗糖尿病药,通过阻断钠和葡萄糖的吸收。为此,本文通过计算机药物设计(CADD)技术,以抑制 SGLT2 蛋白为目标,寻找具有潜在抗糖尿病、心脏保护和利尿特性的天然化合物。通过对庞大的化合物库进行高通量虚拟筛选(HTVS),我们从不同来源发现了几种具有潜在多功能性的生物活性化合物。随后,我们利用分子对接和动力学模拟评估了这些化合物与各自靶点的结合效力和稳定性,并通过 ADMET 预测评估了它们的药代动力学和安全性。最热门的化合物是苯丙氨酸色氨酸、酪氨酸色氨酸、酪氨酸酪氨酸、塞来昔布和三己糖 DIBOA,它们的对接得分从-11.4 到-9.8 kcal/mol不等。分子动力学模拟显示,目标蛋白质与生物化合物之间的相互作用在 100 毫微秒内保持稳定,没有发生明显的构象转变。这些发现为先导物优化和临床前测试奠定了基础。这一严谨的过程确保了潜在治疗方法的安全性和有效性,标志着向开发用于控制糖尿病及其相关并发症的创新治疗方法迈出了重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncovering the antidiabetic potential of heart-friendly and diuretic bioactive compounds through computer-based drug design

Avicenna, a pioneer of modern medicine, recommended diuretic therapy to treat diabetes. Like Avicenna's approach, current medicine frequently prescribes oral antidiabetic pills with diuretic and hypoglycemic effects by blocking the absorption of sodium and glucose. To this end, the paper sought natural compounds with potential antidiabetic, cardioprotective, and diuretic properties through computer-based drug design (CADD) techniques, targeting the inhibition of SGLT2 proteins. We identified several bioactive compounds from various sources exhibiting potential multifunctionality through high-throughput virtual screening (HTVS) of vast compound libraries. Subsequent molecular docking and dynamics simulations were employed to assess these compounds' binding efficacy and stability with their respective targets, alongside ADMET prediction, to evaluate their pharmacokinetic and safety profiles. The top hits, phenylalanyltryptophan, tyrosyl-tryptophan, tyrosyl-tyrosine, celecoxib, and DIBOA trihexose, had superior docking scores ranging from −11,4 to −9,8 kcal/mol. The molecular dynamics simulations displayed steady interactions between target proteins and biocompounds throughout 100 ns without significant conformational shifts. These findings lay the groundwork for lead optimization and preclinical testing. This meticulous process ensures the safety and efficacy of potential treatments, marking a meaningful step toward developing innovative treatments for managing diabetes and its associated health complications.

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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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