Performance exploration of multi-gene panels of alopecia areata susceptibility and drug-binding targets

Frontiers in Physiology, Journal Year: 2025, Volume and Issue: 16

Published: March 27, 2025

Objective This study aims to identify potential target genes and therapeutic drugs for alopecia areata (AA). Methods Utilizing training testing data, we evaluated multi-gene panels derived from commonly upregulated in publicly available AA patient datasets. The functions of these biological processes were analyzed special that may play crucial roles AA. Differences immune cell infiltration between patients healthy controls assessed using gene set variation analysis (GSVA) the Wald test. Signature further validated specific subsets single-cell RNA sequence data. Finally, molecular docking dynamics simulation conducted evaluate interactions protein structures encoded by signature new drug candidates. Results When cut-off value log 2 FoldChage was greater than 1.0, 51 common identified datasets GSE68801 GSE45512 , enrichment process indicated significant involvement cells predictive performance demonstrated excellent accuracy pathways related “regulation T cell-mediated cytotoxicity” “cell killing.” GSVA test NK significantly higher controls. Based on subsets, found within macrophage migration inhibitory factor signaling pathway, cells, CD8 melanocytes observed exclusively but not indicates an important role attack hair follicles via melanocytes. Additionally, selected several biomarkers with interacting chemicals, stability drug–protein binding patterns through simulation, targeted agents. Conclusion In this study, screened key associated drug-like chemicals could serve as therapies

Language: Английский

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The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: 310, P. 143308 - 143308

Published: April 21, 2025

Language: Английский

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Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(7), P. 3047 - 3047

Published: March 26, 2025

In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among models evaluated, Random Forest (RF) algorithm exhibited highest predictive performance, achieving an accuracy 0.97, ROC-AUC score 0.98, and F1-score 0.98. Following model validation, applied it a dataset 14,194 naturally occurring compounds from PubChem. The top-ranked were subsequently subjected molecular docking, which identified Perenniporide B, Phellifuropyranone A, Terrestrol G as most promising candidates, with binding energies -9.17, -9.08, -8.71 kcal/mol, respectively. These formed strong interactions key catalytic residues, suggesting significant inhibitory against viral Furthermore, dynamics simulations confirmed their stability within active site, reinforcing viability antiviral agents. This study demonstrates effectiveness integrating modeling accelerate discovery therapeutic candidates emerging threats.

Language: Английский

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Identification of a new Small Rho GTPase Inhibitor Effective in Glioblastoma Human Cells

European Journal of Medicinal Chemistry, Journal Year: 2025, Volume and Issue: 292, P. 117704 - 117704

Published: April 29, 2025

Language: Английский

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Structural dynamics of Rho GTPases

Journal of Molecular Biology, Journal Year: 2024, Volume and Issue: unknown, P. 168919 - 168919

Published: Dec. 1, 2024

Language: Английский

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