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

Zheng, Linfeng; Qin, Xiao; Wang, Jiao; Zhang, Mingjie; An, Quanlin; Xu, Jinzhi; Qu, Xiaosheng; Cao, Xin; Niu, Bing

doi:10.3390/biom12101470

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…The amino acid residues for MAO-B inhibition are Cys 172, Gln 65, Gln 206, Ile 198, Ile 199, Ile 316, Leu 167, Leu 171, Phe 103, Phe 168, Pro 104, Trp 119, Tyr 326, and Tyr 435 [44,56,57]. On the other hand, safinamide, which was the molecule used as the inhibitor of MAO-B, had the following reported interaction with amino acid residues: Cys 172, Gln 206, Ile 199, Ile 316, Leu 171, Phe 103, Tyr 60, Tyr 326, and Tyr 398 [25].…”

Section: Monoamine Oxidase Pathwaymentioning

confidence: 99%

Ginkgo biloba: Antioxidant Activity and In Silico Central Nervous System Potential

Suárez-González,

Sandoval-Ramírez,

Flores-Hernández

et al. 2023

CIMB

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Ginkgo biloba (GB) extracts have been used in clinical studies as an alternative therapy for Alzheimer’s disease (AD), but the exact bioaction mechanism has not yet been elucidated. In this work, an in silico study on GB metabolites was carried out using SwissTargetPrediction to determine the proteins associated with AD. The resulting proteins, AChE, MAO-A, MAO-B, β-secretase and γ-secretase, were studied by molecular docking, resulting in the finding that kaempferol, quercetin, and luteolin have multitarget potential against AD. These compounds also exhibit antioxidant activity towards reactive oxygen species (ROS), so antioxidant tests were performed on the extracts using the DPPH and ABTS techniques. The ethanol and ethyl acetate GB extracts showed an important inhibition percentage, higher than 80%, at a dose of 0.01 mg/mL. The effect of GB extracts on AD resulted in multitarget action through two pathways: firstly, inhibiting enzymes responsible for degrading neurotransmitters and forming amyloid plaques; secondly, decreasing ROS in the central nervous system (CNS), reducing its deterioration, and promoting the formation of amyloid plaques. The results of this work demonstrate the great potential of GB as a medicinal plant.

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Section: Monoamine Oxidase Pathwaymentioning

confidence: 99%

Ginkgo biloba: Antioxidant Activity and In Silico Central Nervous System Potential

Suárez-González,

Sandoval-Ramírez,

Flores-Hernández

et al. 2023

CIMB

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“…Machine learning algorithms are increasingly being used to deal with the growth of huge data in the life sciences including drug design, protein prediction, epidemic prediction [ 26 – 44 ]. In this paper, ten machine learning algorithms including K nearest neighbor (KNN), Adaboost, Bagging, Random Forest (RF), Random Trees (RT), AD tree, C4.5, Bayes net, Support vector ma-chine(SVM) and Artificial neural network(ANN) were employed to building a prediction model for selecting the optimal VEGFR-2 inhibitors.…”

Section: Methodsmentioning

confidence: 99%

QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking

Ding,

Xing,

Zou

et al. 2024

BMC Chemistry

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VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.

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Design, synthesis and antimicrobial activity of novel berberine derivatives

Zhang,

Li,

et al. 2024

Pest Management Science

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BACKGROUNDThe threats to the safety of humans and the environment and the resistance of agricultural chemicals to plant pathogenic fungi and bacteria highlight an urgent need to find safe and efficient alternatives to chemical fungicides and bactericides. In this study, a series of Berberine (BBR) derivatives were designed, synthesized and evaluated for in vitro and in vivo antimicrobial activity against plant pathogenic fungi and bacteria.RESULTSBioassay results indicated that compounds A11, A14, A20, A21, A22, A25, A26, E1, E2, E3, Z1 and Z2 showed high inhibitory activity against Sclerotinia sclerotiorum and Botrytis cinerea. Especially, A25 showed a broad spectrum and the highest antifungal activity among these compounds. Its EC50 value against Botrytis cinerea was 1.34 μg mL−1. Compound E6 possessed high inhibitory activity against Xanthomonas oryzae and Xanthomonas Campestris, with MIC90 values of 3.12 μg mL−1 and 1.56 μg mL−1. A Topomer CoMFA model was generated for 3D‐QSAR studies based on anti‐B. cinerea effects, with high predictive accuracy, showed that the addition of an appropriate substituent group at the para‐position of benzyl of BBR derivatives could effectively improve the anti‐B. cinerea activity. In addition, compound A25 could significantly inhibit the spore germination of Botrytis cinerea at low concentration, and compound F4 exhibited remarkable curative and protective efficiencies on rice bacterial leaf blight.CONCLUSIONThis study indicates that the BBR derivatives are hopeful for further exploration as the lead compound with novel antimicrobial agents. © 2024 Society of Chemical Industry.

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

Cited by 6 publications

References 39 publications

Ginkgo biloba: Antioxidant Activity and In Silico Central Nervous System Potential

Ginkgo biloba: Antioxidant Activity and In Silico Central Nervous System Potential

QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking

Design, synthesis and antimicrobial activity of novel berberine derivatives

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