Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Jokinen, Elmeri M.; Niemeläinen, Miika; Kurkinen, Sami T.; Lehtonen, Jukka V.; Lätti, Sakari; Postila, Pekka A.; Pentikäinen, Olli T.; Niinivehmas, Sanna

doi:10.3390/molecules28083420

Cited by 3 publications

(1 citation statement)

References 62 publications

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“…Different in silico methods have been used to identify selective ligands for the nuclear receptor (NR) superfamily members [ 32 , 33 ]. Virtual screening (VS) approach has been used to recognize novel ligands for these receptors with a large variety of biological activities [ 34 – 44 ]. Most of the compounds obtained from this screening process had proper activities both in vitro and in vivo confirming the in silico predictions.…”

Section: Introductionmentioning

confidence: 99%

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Ghorayshian,

Danesh,

Mostashari-Rad

et al. 2023

PLoS ONE

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Nuclear retinoic acid receptors (RARs) are ligand-dependent transcription factors involved in various biological processes, such as embryogenesis, cell proliferation, differentiation, reproduction, and apoptosis. These receptors are regulated by retinoids, i.e., retinoic acid (RA) and its analogs, as receptor agonists. RAR agonists are promising therapeutic agents for the treatment of serious dermatological disorders, including some malignant conditions. By inducing apoptosis, they are able to inhibit the proliferation of diverse cancer cell lines. Also, RAR agonists have recently been identified as therapeutic options for some neurodegenerative diseases. These features make retinoids very attractive molecules for medical purposes. Synthetic selective RAR agonists have several advantages over endogenous ones, but they suffer poor pharmacokinetic properties. These compounds are normally lipophilic acids with unfavorable drug-like features such as poor oral bioavailability. Recently, highly selective, potent, and less toxic RAR agonists with proper lipophilicity, thus, good oral bioavailability have been developed for some therapeutic applications. In the present study, ligand and structure-based virtual screening technique was exploited to introduce some novel RARα agonists. Pharmacokinetic assessment was also performed in silico to suggest those compounds which have optimized drug-like features. Finally, two compounds with the best in silico pharmacological features are proposed as lead molecules for future development of RARα agonists.

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Section: Introductionmentioning

confidence: 99%

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Ghorayshian,

Danesh,

Mostashari-Rad

et al. 2023

PLoS ONE

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Building shape-focused pharmacophore models for effective docking screening

Moyano-Gómez,

Lehtonen,

Pentikäinen

et al. 2024

J Cheminform

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The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins’ inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub (https://github.com/jvlehtonen/overlap-toolkit). Scientific contribution This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing. Graphical Abstract

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Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

Sharma,

Panwar,

Madhavi

et al. 2024

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Background: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses. Method: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα. Results: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity. Conclusion: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

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Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Cited by 3 publications

References 62 publications

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Building shape-focused pharmacophore models for effective docking screening

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

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