Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches
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. Th...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Biomolecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-273X/12/10/1470 |
| _version_ | 1797474875479162880 |
|---|---|
| author | Linfeng Zheng Xiangyang Qin Jiao Wang Mengying Zhang Quanlin An Jinzhi Xu Xiaosheng Qu Xin Cao Bing Niu |
| author_facet | Linfeng Zheng Xiangyang Qin Jiao Wang Mengying Zhang Quanlin An Jinzhi Xu Xiaosheng Qu Xin Cao Bing Niu |
| author_sort | Linfeng Zheng |
| collection | DOAJ |
| description | 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<sup>2</sup> = 0.612 (cross-validated correlation coefficient) and r<sup>2</sup> = 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. |
| first_indexed | 2024-03-09T20:37:17Z |
| format | Article |
| id | doaj.art-bdae2cb72c7a4537a0916e1df324f50c |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-09T20:37:17Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-bdae2cb72c7a4537a0916e1df324f50c2023-11-23T23:09:11ZengMDPI AGBiomolecules2218-273X2022-10-011210147010.3390/biom12101470Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy ApproachesLinfeng Zheng0Xiangyang Qin1Jiao Wang2Mengying Zhang3Quanlin An4Jinzhi Xu5Xiaosheng Qu6Xin Cao7Bing Niu8School of Life Science, Shanghai University, Shanghai 200444, ChinaDepartment of Chemistry, School of Pharmacy, Air Force Medical University, Xi’an 710032, ChinaSchool of Life Science, Shanghai University, Shanghai 200444, ChinaSchool of Life Science, Shanghai University, Shanghai 200444, ChinaInstitute of Clinical Science, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai 200444, ChinaInstitute of Clinical Science, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai 200444, ChinaNational Engineering Laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, Nanning 530023, ChinaInstitute of Clinical Science, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai 200444, ChinaSchool of Life Science, Shanghai University, Shanghai 200444, ChinaAlzheimer’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<sup>2</sup> = 0.612 (cross-validated correlation coefficient) and r<sup>2</sup> = 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.https://www.mdpi.com/2218-273X/12/10/1470Alzheimer’s disease (AD)monoamine oxidase B (MAO-B) inhibitorsmachine learningmolecular dockingfluorescence quenching |
| spellingShingle | Linfeng Zheng Xiangyang Qin Jiao Wang Mengying Zhang Quanlin An Jinzhi Xu Xiaosheng Qu Xin Cao Bing Niu Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches Biomolecules Alzheimer’s disease (AD) monoamine oxidase B (MAO-B) inhibitors machine learning molecular docking fluorescence quenching |
| title | Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches |
| title_full | Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches |
| title_fullStr | Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches |
| title_full_unstemmed | Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches |
| title_short | Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches |
| title_sort | discovery of mao b inhibitor with machine learning topomer comfa molecular docking and multi spectroscopy approaches |
| topic | Alzheimer’s disease (AD) monoamine oxidase B (MAO-B) inhibitors machine learning molecular docking fluorescence quenching |
| url | https://www.mdpi.com/2218-273X/12/10/1470 |
| work_keys_str_mv | AT linfengzheng discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT xiangyangqin discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT jiaowang discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT mengyingzhang discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT quanlinan discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT jinzhixu discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT xiaoshengqu discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT xincao discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches AT bingniu discoveryofmaobinhibitorwithmachinelearningtopomercomfamoleculardockingandmultispectroscopyapproaches |