School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
Phys Chem Chem Phys. 2019 Feb 20;21(8):4258-4267. doi: 10.1039/c8cp05647a.
The recent discovery of the role of adenosine-analogues as neuroprotectants and cognitive enhancers has sparked interest in these molecules as new therapeutic drugs. Understanding the behavior of these molecules in solution and predicting their ability to self-assemble will accelerate new discoveries. We propose a computational approach based on density functional theory, a polarizable continuum solvation description of the aqueous environment, and an efficient search procedure to probe the potential energy surface, to determine the structure and thermodynamic stability of molecular clusters of adenosine analogues in solution, using caffeine as a model. The method was validated as a tool for the prediction of the impact of small structural variations on self-assembly using paraxanthine. The computational results were supported by isothermal titration calorimetry experiments. The thermodynamic parameters enabled the quantification of the actual percentage of dimer present in solution as a function of concentration. The data suggest that both caffeine and paraxanthine are present at concentrations comparable to the ones found in biological samples.
最近发现腺苷类似物具有神经保护和认知增强作用，这激发了人们对这些分子作为新型治疗药物的兴趣。了解这些分子在溶液中的行为并预测它们自组装的能力将加速新的发现。我们提出了一种基于密度泛函理论的计算方法，该方法对水相环境进行了极化连续体溶剂化描述，并采用高效搜索程序来探测潜在的能量表面，以确定咖啡因模型中腺苷类似物分子簇在溶液中的结构和热力学稳定性。该方法通过对茶碱进行小结构变化对自组装影响的预测得到了验证。计算结果得到了等温滴定量热实验的支持。热力学参数使我们能够定量确定溶液中实际存在的二聚体的百分比浓度。数据表明，咖啡因和茶碱的浓度与在生物样本中发现的浓度相当。
