Simeon Saw, Anuwongcharoen Nuttapat, Shoombuatong Watshara, Malik Aijaz Ahmad, Prachayasittikul Virapong, Wikberg Jarl E S, Nantasenamat Chanin
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.
Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.
PeerJ. 2016 Aug 9;4:e2322. doi: 10.7717/peerj.2322. eCollection 2016.
Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R (2), [Formula: see text] and [Formula: see text] values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R (2), [Formula: see text] and [Formula: see text] values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard-Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of -12.2, -12.0 and -12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π-π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.
阿尔茨海默病(AD)是一种慢性神经退行性疾病,会导致神经元细胞逐渐丧失。目前存在多种关于AD的假说(例如胆碱能假说、淀粉样蛋白假说、tau蛋白假说等)。根据胆碱能假说,胆碱缺乏是导致AD的原因;因此,抑制乙酰胆碱酯酶(AChE)是治疗AD的一种有前景的治疗策略。乙酰胆碱酯酶(AChE)是一种催化神经递质乙酰胆碱分解的酶,乙酰胆碱对认知和记忆至关重要。从ChEMBL获得了一个包含2570种化合物的大型非冗余数据集,这些化合物具有针对AChE的报告IC50值,并用于定量构效关系(QSAR)研究,以便深入了解它们的生物活性来源。用一组12个指纹描述符描述AChE抑制剂,并使用随机森林从100个不同的数据划分构建预测模型。生成的模型在训练集、10倍交叉验证集和外部集上的R(2)、[公式:见原文]和[公式:见原文]值分别在0.66 - 0.93、0.55 - 0.79和0.56 - 0.81范围内。根据经合组织指南选择使用子结构计数构建的最佳模型,其R(2)、[公式:见原文]和[公式:见原文]值分别为0.92±0.01、0.78±0.06和0.78±0.05。此外,应用Y打乱来评估预测模型偶然相关性的可能性。随后,对子结构指纹计数进行了深入分析,以提供有关AChE抑制剂抑制活性的信息性见解。此外,对活性化合物进行肯纳德 - 斯通采样,选择30种不同的化合物进行进一步的分子对接研究,以便深入了解AChE抑制的起源结构。来自多样性集的化合物的位点 - 部分映射揭示了三个结合锚点,包括氢键和范德华相互作用。分子对接显示,化合物13、5和28对人AChE的结合能最低,分别为 - 12.2、 - 12.0和 - 12.0 kcal/mol,这是由结合口袋内的氢键、π - π堆积和疏水相互作用调节的。这些信息可作为设计新型强效AChE抑制剂的指导原则。