New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods
To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure–activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/11/9583 |
| _version_ | 1797597358662352896 |
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| author | Liliana Pacureanu Alina Bora Luminita Crisan |
| author_facet | Liliana Pacureanu Alina Bora Luminita Crisan |
| author_sort | Liliana Pacureanu |
| collection | DOAJ |
| description | To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure–activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R<sup>2</sup> = 0.900 (training set); Q<sup>2</sup> = 0.774 and Pearson’s R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases. |
| first_indexed | 2024-03-11T03:05:06Z |
| format | Article |
| id | doaj.art-e266b7c369fc41efabcafaf5be22f9c3 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T03:05:06Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-e266b7c369fc41efabcafaf5be22f9c32023-11-18T08:01:06ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-05-012411958310.3390/ijms24119583New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico MethodsLiliana Pacureanu0Alina Bora1Luminita Crisan2“Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania“Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania“Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, RomaniaTo facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure–activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R<sup>2</sup> = 0.900 (training set); Q<sup>2</sup> = 0.774 and Pearson’s R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.https://www.mdpi.com/1422-0067/24/11/9583monoamine oxidase Bselectivityactivity cliffECFP4 |
| spellingShingle | Liliana Pacureanu Alina Bora Luminita Crisan New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods International Journal of Molecular Sciences monoamine oxidase B selectivity activity cliff ECFP4 |
| title | New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods |
| title_full | New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods |
| title_fullStr | New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods |
| title_full_unstemmed | New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods |
| title_short | New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods |
| title_sort | new insights on the activity and selectivity of mao b inhibitors through in silico methods |
| topic | monoamine oxidase B selectivity activity cliff ECFP4 |
| url | https://www.mdpi.com/1422-0067/24/11/9583 |
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