Department Of Pharmaceutical Sciences and Natural Products

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Subject: search.filters.subject.Molecular dynamics simulations

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    Probing the molecular mechanisms of ?-synuclein inhibitors unveils promising natural candidates through machine-learning QSAR, pharmacophore modeling, and molecular dynamics simulations
    (Institute for Ionics, 2023-07-18T00:00:00) Boulaamane, Yassir; Jangid, Kailash; Britel, Mohammed Reda; Maurady, Amal
    Parkinson�s disease is characterized by a multifactorial nature that is linked to different pathways. Among them, the abnormal deposition and accumulation of ?-synuclein fibrils is considered a neuropathological hallmark of Parkinson�s disease. Several synthetic and natural compounds have been tested for their potency to inhibit the aggregation of ?-synuclein. However, the molecular mechanisms responsible for the potency of these drugs to further rationalize their development and optimization are yet to be determined. To enhance our understanding of the structural requirements necessary for modulating the aggregation of ?-synuclein fibrils, we retrieved a large dataset of ?-synuclein inhibitors with their reported potency from the ChEMBL database to explore their chemical space and to generate QSAR models for predicting new bioactive compounds. The best performing QSAR model was applied to the LOTUS natural products database to screen for potential ?-synuclein inhibitors followed by a pharmacophore design using the representative compounds sampled from each cluster in the ChEMBL dataset. Five natural products were retained after molecular docking studies displaying a binding affinity of ? 6.0�kcal/mol or lower. ADMET analysis revealed satisfactory properties and predicted that all the compounds can cross the blood�brain barrier and reach their target. Finally, molecular dynamics simulations demonstrated the superior stability of LTS0078917 compared to the clinical candidate, Anle138b. We found that LTS0078917 shows promise in stabilizing the ?-synuclein monomer by specifically binding to its hairpin-like coil within the N-terminal region. Our dynamic analysis of the inhibitor-monomer complex revealed a tendency towards a more compact conformation, potentially reducing the likelihood of adopting an elongated structure that favors the formation and aggregation of pathological oligomers. These findings offer valuable insights for the development of novel ?-synuclein inhibitors derived from natural sources. Graphical abstract: [Figure not available: see fulltext.]. � 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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    Multifaceted 3D-QSAR analysis for the identification of pharmacophoric features of biphenyl analogues as aromatase inhibitors
    (Taylor and Francis Ltd., 2021-12-29T00:00:00) Banjare, Laxmi; Singh, Yogesh; Verma, Sant Kumar; Singh, Atul Kumar; Kumar, Pradeep; Kumar, Shashank; Jain, Akhlesh Kumar; Thareja, Suresh
    Aromatase, a cytochrome P450 enzyme, is responsible for the conversion of androgens to estrogens, which fuel the multiplication of cancerous cells. Inhibition of estrogen biosynthesis by aromatase inhibitors (AIs) is one of the highly advanced therapeutic approach available for the treatment of estrogen-positive breast cancer. Biphenyl moiety aids lipophilicity to the conjugated scaffold and enhances the accessibility of the ligand to the target. The present study is focused on the investigation of, the mode of binding of biphenyl with aromatase, prediction of ligand-target binding affinities, and pharmacophoric features essential for favorable for aromatase inhibition. A multifaceted 3D-QSAR (SOMFA, Field and Gaussian) along with molecular docking, molecular dynamic simulations and pharmacophore mapping were performed on a series of biphenyl bearing molecules (1�33) with a wide range of aromatase inhibitory activity (0.15�920 nM). Among the generated 3D-QSAR models, the Force field-based 3D-QSAR model (R 2 = 0.9151) was best as compared to SOMFA and Gaussian Field (R 2=0.7706, 0.9074, respectively). However, all the generated 3D-QSAR models were statistically fit, robust enough, and reliable to explain the variation in biological activity in relation to pharmacophoric features of dataset molecules. A four-point pharmacophoric features with three acceptor sites (A), one aromatic ring (R) features, AAAR_1, were obtained with the site and survival score values 0.890 and 4.613, respectively. The generated 3D-QSAR plots in the study insight into the structure�activity relationship of dataset molecules, which may help in the designing of potent biphenyl derivatives as newer inhibitors of aromatase. Communicated by Ramaswamy H. Sarma. � 2021 Informa UK Limited, trading as Taylor & Francis Group.