Padiadpu Jyothi, Mishra Madhulika, Sharma Eshita, Mala Uchurappa, Somasundaram Kumar, Chandra Nagasuma
Department of Biochemistry, IISc, Bangalore 560012, India.
Supercomputer Education and Research Centre, IISc, Bangalore 560012, India.
J Chem Inf Model. 2016 May 23;56(5):843-53. doi: 10.1021/acs.jcim.5b00733. Epub 2016 Apr 18.
The biosynthesis of NAD constitutes an important metabolic module in the cell, since NAD is an essential cofactor involved in several metabolic reactions. NAD concentrations are known to be significantly increased in several cancers, particularly in glioma, consistent with the observation of up-regulation of several enzymes of the network. Modulating NAD biosynthesis in glioma is therefore an attractive therapeutic strategy. Here we report reconstruction of a biochemical network of NAD biosynthesis consisting of 22 proteins, 36 metabolites, and 86 parameters, tuned to mimic the conditions in glioma. Kinetic simulations of the network provide comprehensive insights about the role of individual enzymes. Further, quantitative changes in the same network between different states of health and disease enable identification of drug targets, based on specific alterations in the given disease. Through simulations of enzyme inhibition titrations, we identify NMPRTase as a potential drug target, while eliminating other possible candidates NMNAT, NAPRTase, and NRK. We have also simulated titrations of both binding affinities as well as inhibitor concentrations, which provide insights into the druggability limits of the target, a novel aspect that can provide useful guidelines for designing inhibitors with optimal affinities. Our simulations suggest that an inhibitor affinity of 10 nM used in a concentration range of 0.1 to 10 μM achieves a near maximal inhibition response for NMPRTase and that increasing the affinity any further is not likely to have a significant advantage. Thus, the quantitative appreciation defines a maximal extent of inhibition possible for a chosen enzyme in the context of its network. Knowledge of this type enables an upper affinity threshold to be defined as a goal in lead screening and refinement stages in drug discovery.
烟酰胺腺嘌呤二核苷酸（NAD）的生物合成是细胞中一个重要的代谢模块，因为NAD是参与多种代谢反应的必需辅助因子。已知在几种癌症中，特别是在胶质瘤中，NAD浓度会显著增加，这与该网络中几种酶的上调观察结果一致。因此，调节胶质瘤中的NAD生物合成是一种有吸引力的治疗策略。在此，我们报告了一个由22种蛋白质、36种代谢物和86个参数组成的NAD生物合成生化网络的重建，该网络经过调整以模拟胶质瘤中的条件。该网络的动力学模拟提供了关于单个酶作用的全面见解。此外，同一网络在健康和疾病不同状态之间的定量变化能够基于给定疾病中的特定改变来识别药物靶点。通过酶抑制滴定模拟，我们确定NMPRTase为潜在的药物靶点，同时排除了其他可能的候选靶点NMNAT、NAPRTase和NRK。我们还模拟了结合亲和力以及抑制剂浓度的滴定，这为靶点的成药极限提供了见解，这是一个新的方面，可以为设计具有最佳亲和力的抑制剂提供有用的指导。我们的模拟表明，在0.1至10μM的浓度范围内使用10 nM的抑制剂亲和力可实现对NMPRTase的近乎最大抑制反应，进一步提高亲和力不太可能有显著优势。因此，定量评估定义了在其网络背景下所选酶可能的最大抑制程度。这种类型的知识能够将一个较高的亲和力阈值定义为药物发现中先导筛选和优化阶段的目标。
