De Priyanka, Roy Kunal
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032 India.
In Silico Pharmacol. 2023 Apr 4;11(1):9. doi: 10.1007/s40203-023-00146-4. eCollection 2023.
The neurotransmitter acetylcholine (ACh) plays a ubiquitous role in cognitive functions including learning and memory with widespread innervation in the cortex, subcortical structures, and the cerebellum. Cholinergic receptors, transporters, or enzymes associated with many neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are potential imaging targets. In the present study, we have developed 2D-quantitative structure-activity relationship (2D-QSAR) models for 19 positron emission tomography (PET) imaging agents targeted against presynaptic vesicular acetylcholine transporter (VAChT). VAChT assists in the transport of ACh into the presynaptic storage vesicles, and it becomes one of the main targets for the diagnosis of various neurodegenerative diseases. In our work, we aimed to understand the important structural features of the PET imaging agents required for their binding with VAChT. This was done by feature selection using a Genetic Algorithm followed by the Best Subset Selection method and developing a Partial Least Squares- based 2D-QSAR model using the best feature combination. The developed QSAR model showed significant statistical performance and reliability. Using the features selected in the 2D-QSAR analysis, we have also performed similarity-based chemical read-across predictions and obtained encouraging external validation statistics. Further, we have also performed molecular docking analysis to understand the molecular interactions occurring between the PET imaging agents and the VAChT receptor. The molecular docking results were correlated with the QSAR features for a better understanding of the molecular interactions. This research serves to fulfill the experimental data gap, highlighting the applicability of computational methods in the PET imaging agents' binding affinity prediction.
The online version contains supplementary material available at 10.1007/s40203-023-00146-4.
神经递质乙酰胆碱（ACh）在包括学习和记忆在内的认知功能中发挥着普遍作用，在皮层、皮层下结构和小脑中广泛分布。与包括阿尔茨海默病（AD）和帕金森病（PD）在内的许多神经退行性疾病相关的胆碱能受体、转运体或酶是潜在的成像靶点。在本研究中，我们针对19种靶向突触前囊泡乙酰胆碱转运体（VAChT）的正电子发射断层扫描（PET）成像剂开发了二维定量构效关系（2D-QSAR）模型。VAChT有助于将ACh转运到突触前储存囊泡中，它成为各种神经退行性疾病诊断的主要靶点之一。在我们的工作中，我们旨在了解PET成像剂与VAChT结合所需的重要结构特征。这是通过使用遗传算法进行特征选择，然后采用最佳子集选择方法，并使用最佳特征组合开发基于偏最小二乘法的2D-QSAR模型来实现的。所开发的QSAR模型显示出显著的统计性能和可靠性。利用在2D-QSAR分析中选择的特征，我们还进行了基于相似性的化学类推预测，并获得了令人鼓舞的外部验证统计数据。此外，我们还进行了分子对接分析，以了解PET成像剂与VAChT受体之间发生的分子相互作用。分子对接结果与QSAR特征相关联，以便更好地理解分子相互作用。本研究有助于填补实验数据空白，突出了计算方法在PET成像剂结合亲和力预测中的适用性。
在线版本包含可在10.1007/s40203-023-00146-4获取的补充材料。
