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. 2022 Oct 13;12(10):1470.
doi: 10.3390/biom12101470.

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

Linfeng Zheng  1   2 , Xiangyang Qin  3 , Jiao Wang  1 , Mengying Zhang  1 , Quanlin An  4 , Jinzhi Xu  4 , Xiaosheng Qu  5 , Xin Cao  4 , Bing Niu  1
Affiliations

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

Linfeng Zheng et al. Biomolecules. .

Abstract

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 q2 = 0.612 (cross-validated correlation coefficient) and r2 = 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.

Keywords: Alzheimer’s disease (AD); fluorescence quenching; machine learning; molecular docking; monoamine oxidase B (MAO-B) inhibitors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The relationship of activity of different molecule descriptors.
Figure 2
Figure 2
Experimental data versus predicted data from the Topomer CoMFA Model 2.
Figure 3
Figure 3
3D contour maps of Topomer CoMFA Model 2 for R1 (A,B) and R2 (C,D) of compound 63. ((A,C) represent steric contour maps. (B,D) represent electrostatic field maps.).
Figure 4
Figure 4
The structures of potential compounds
Figure 5
Figure 5
The docking results of inhibitors with MAO-B. (A). Compound 63 with MAO-B at GLN206 and GLN65. (B). Compound 64 with MAO-B at GLN206 and GLN65. (C). Compound 65 with MAO-B at GLN206 and GLN65. (D). Compound 67 with MAO-B at GLN206 and GLN65. (E). Compound 107 with MAO-B at GLN206. Hydrogen bonding is depicted as yellow dashed lines.
Figure 6
Figure 6
The docking results of LB with MAO-B and the binding pock of MAO-B. ((A). The structure of LB, (B). The binding pocket of MAO-B, (C). The interaction of MAO-B and LB).
Figure 7
Figure 7
The fluorescence quenching of MAO-B by LB. (A). The fluorescence emission spectra of MAO-B-LB with an excitation wavelength of 280 nm, a→i were the fluorescence spectra with 0→8 μL LB added, (B). 3D fluorescence spectra of MAO-B, (C). 3D fluorescence spectra of MAO-B-LB.).
Figure 8
Figure 8
The unmodified Stern–Volmer curves of MAO-B fluorescence quenched by LB.
Figure 9
Figure 9
Double logarithmic curves (A) and the modified Stern–Volmer curves (B) of MAO-B fluorescence quenched by LB.
Figure 10
Figure 10
UV–vis absorption spectra of MAO-B in the presence of different LB concentrations. a→i were the UV–vis absorption spectra with 0→8 μL LB added.
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