School of Life Science, Shanghai University, Shanghai 200444, China.
Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
Biomolecules. 2022 Oct 13;12(10):1470. doi: 10.3390/biom12101470.
Alzheimer's disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q = 0.612 (cross-validated correlation coefficient) and r = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry.
阿尔茨海默病（AD）是最常见的痴呆症类型，严重扰乱了正常生活。单胺氧化酶-B（MAO-B）是治疗 AD 的重要靶点。在这项研究中，应用机器学习方法研究了 MAO-B 抑制剂的识别模型。结果表明，应用 K-最近邻（KNN）算法的 MAO-B 抑制剂识别模型在 10 折交叉验证测试和独立测试集中的预测准确性分别为 94.1%和 88.0%。其次，用 Topomer CoMFA 模型研究了 MAO-B 的定量活性预测模型。采用两种独立的切割模式方法来预测 MAO-B 抑制剂的活性。结果表明，q = 0.612（交叉验证相关系数）和 r = 0.824（非交叉验证相关系数）的切割模型分别适用于训练集和测试集。此外，还进行了分子对接分析 MAO-B 与抑制剂的相互作用。最后，基于我们提出的预测模型，预测 1-(4-羟基苯基)-3-(2,4,6-三甲氧基苯基)丙-1-酮（LB）可能是一种潜在的 MAO-B 抑制剂，并通过包括荧光光谱和紫外分光光度法在内的多种光谱方法进行了验证。
