Monash Institute of Pharmaceutical Sciences, Monash University, Parkville; Latrobe Institute for Molecular Science, La Trobe University, Bundoora, Australia; School of Pharmacy, University of Nottingham, Nottingham, UK.
Early Drug Development, Air Liquide Santé International, Les loges-en-Josas, France.
Med Gas Res. 2023 Jan-Mar;13(1):33-38. doi: 10.4103/2045-9912.333858.
In a previous study, in silico screening of the binding of almost all proteins in the Protein Data Bank to each of the five noble gases xenon, krypton, argon, neon, and helium was reported. This massive and rich data set requires analysis to identify the gas-protein interactions that have the best binding strengths, those where the binding of the noble gas occurs at a site that can modulate the function of the protein, and where this modulation might generate clinically relevant effects. Here, we report a preliminary analysis of this data set using a rational, heuristic score based on binding strength and location. We report a partial prioritized list of xenon protein targets and describe how these data can be analyzed, using arginase and carbonic anhydrase as examples. Our aim is to make the scientific community aware of this massive, rich data set and how it can be analyzed to accelerate future discoveries of xenon-induced biological activity and, ultimately, the development of new "atomic" drugs.
在之前的一项研究中,报告了对蛋白质数据库中几乎所有蛋白质与氙气、氪气、氩气、氖气和氦气这五种惰性气体中的每一种进行结合的计算机模拟筛选。这个海量且丰富的数据集需要进行分析,以确定具有最佳结合强度的气体-蛋白质相互作用,即惰性气体结合发生在能够调节蛋白质功能的部位,并且这种调节可能产生临床相关的效果。在这里,我们使用基于结合强度和位置的合理启发式评分方法对该数据集进行了初步分析。我们报告了一个部分优先的氙气蛋白靶目标列表,并描述了如何使用精氨酸酶和碳酸酐酶作为示例来分析这些数据。我们的目的是使科学界意识到这个庞大而丰富的数据集,以及如何对其进行分析,以加速未来发现氙气诱导的生物活性,并最终开发新的“原子”药物。