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Riyed, T. H.

Publications and source records attributed to Riyed, T. H..

2 recordsLinked to original sources

Peptide allosteric inhibitor of TNFR1 signaling attenuates inflammation and rheumatoid arthritis pathology in human TNF transgenic mice

Inhibition of tumor necrosis factor receptor 1 (TNFR1) represents a major therapeutic strategy for chronic autoimmune and inflammatory diseases such as rheumatoid arthritis (RA). As current anti-TNF therapies can cause adverse side effects due to global blockade of the ligand, receptor-specific inhibition of TNFR1 signaling has emerged as a highly sought-after strategy. We have recently identified a novel peptide-based allosteric inhibitor, FKC (FKCRRWQWRMKK), that targets TNFR1 conformationally active region to alter receptor conformational states and disable receptor-ligand signaling complex. Here, we evaluated the therapeutic efficacy of FKC in a human TNF (hTNF) transgenic mouse model of RA. FKC treatment improves clinical RA scores in hTNF mice, accompanied by enhanced grip strength and increased walking distance. Importantly, FKC treatment inhibits TNF/TNFR1-mediated inflammation and attenuates RA pathology in hTNF mice. Together, our findings establish FKC as a promising new class of peptide-based therapeutics for chronic inflammatory diseases through selective inhibition of TNFR1 signaling.

immunology↗

Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation

Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the worlds population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need to seek potential new therapeutic candidates, particularly those of natural sources that can treat the condition with minimal side effects. To address this challenge, this study employed a deep-learning (DL) based approach to conduct a virtual assessment of natural compounds against the Tumor Necrosis Factor-alpha (TNF-) protein. TNF- stands out as the primary pro-inflammatory cytokine, crucial in the development of RA. Our predictive model demonstrated appreciable performance, achieving MSE of 0.6, MAPE of 10%, and MAE of 0.5. The model was then deployed to screen a comprehensive set of 2563 natural compounds obtained from the Selleckchem database. Utilizing their predicted bioactivity (pIC50), the top 128 compounds were identified. Among them, 68 compounds were taken for further analysis based on drug-likeness analysis. Subsequently, selected compounds underwent additional evaluation using molecular docking (< - 8.7 kcal/mol) and ADMET resulting in four compounds posing nominal toxicity, which were finally subjected to MD simulation for 200 ns. Later on, the stability of complexes was assessed via analysis encompassing RMSD, RMSF, Rg, H-Bonds, SASA, and Essential Dynamics. Ultimately, based on the total binding free energy estimated using the MM/GBSA method, Imperialine, Veratramine, and Gelsemine are proven to be potential natural inhibitors of TNF-.

bioinformatics↗