Department of Biochemistry and Mathematics , University of Michigan , Ann Arbor , Michigan 48109 , United States.
J Phys Chem B. 2019 Jul 18;123(28):6034-6041. doi: 10.1021/acs.jpcb.9b04551. Epub 2019 Jul 3.
Glycolytic enzyme fructose-bisphosphate aldolase A is an emerging therapeutic target in cancer. Recently, we have solved the crystal structure of murine aldolase in complex with naphthalene-2,6-diyl bisphosphate (ND1) that served as a template of the design of bisphosphate-based inhibitors. In this work, a series of ND1 analogues containing difluoromethylene (-CF), methylene (-CH), or aldehyde substitutions were designed. All designed compounds were studied using molecular dynamics (MD) simulations with the AMOEBA force field. Both energetics and structural analyses have been done to understand the calculated binding free energies. The average distances between ligand and protein atoms for ND1 were very similar to those for the ND1 crystal structure, which indicates that our MD simulation is sampling the correct conformation well. CF insertion lowers the binding free energy by 10-15 kcal/mol, while CF substitution slightly increases the binding free energy, which matches the experimental measurement. In addition, we found that NDB with two CF insertions, the strongest binder, is entropically driven, while others including NDA with one CF insertion are all enthalpically driven. This work provides insights into the mechanisms underlying protein-phosphate binding and enhances the capability of applying computational and theoretical frameworks to model, predict, and design diagnostic strategies targeting cancer.
糖酵解酶果糖-1,6-二磷酸醛缩酶 A 是癌症治疗的新兴靶点。最近,我们已经解决了与萘-2,6-二基双磷酸(ND1)复合物的鼠醛缩酶晶体结构,该结构可作为双磷酸盐类抑制剂设计的模板。在这项工作中,设计了一系列含有二氟亚甲基(-CF)、亚甲基(-CH)或醛取代基的 ND1 类似物。所有设计的化合物均使用 AMOEBA 力场进行分子动力学(MD)模拟研究。通过能量和结构分析来理解计算的结合自由能。ND1 的配体与蛋白质原子之间的平均距离与 ND1 晶体结构非常相似,这表明我们的 MD 模拟很好地采样了正确的构象。CF 插入使结合自由能降低了 10-15 kcal/mol,而 CF 取代略微增加了结合自由能,这与实验测量结果相符。此外,我们发现具有两个 CF 插入的最强结合剂 NDB 是熵驱动的,而其他包括一个 CF 插入的 NDA 都是焓驱动的。这项工作深入了解了蛋白质-磷酸结合的机制,并增强了应用计算和理论框架来模拟、预测和设计针对癌症的诊断策略的能力。