Unveiling Phytoconstituents with Inhibitory Potential Against Tyrosine-Protein Kinase Fyn: A Comprehensive Virtual Screening Approach Targeting Alzheimer's Disease

In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study

This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.

Pharmacological inhibition of Src family kinases attenuates hyperuricemic nephropathy

Hyperuricemia is an independent risk factor for chronic kidney disease and contributes to renal fibrosis. This study aims to investigate the effect of Src family kinase (SFK) inhibition on the development of hyperuricemic nephropathy (HN) and the mechanisms involved. In a rat model of HN, feeding rats a mixture of adenine and potassium oxonate increased Src phosphorylation, severe glomerular sclerosis, and renal interstitial fibrosis, accompanied by renal dysfunction and increased urine microalbumin excretion. Administration of PP1, a highly selective SFK inhibitor, prevented renal dysfunction, reduced urine microalbumin, and inhibited activation of renal interstitial fibroblasts and expression of extracellular proteins. PP1 treatment also inhibited hyperuricemia-induced activation of the TGF-β1/Smad3, STAT3, ERK1/2, and NF-κB signaling pathways and expression of multiple profibrogenic cytokines/chemokines in the kidney. Furthermore, PP1 treatment significantly reduced serum uric acid levels and xanthine oxidase activity. Thus, blocking Src can attenuate development of HN via a mechanism associated with the suppression of TGF-β1 signaling, inflammation, and uric acid production. The results suggest that Src inhibition might be a promising therapeutic strategy for HN.