Polysaccharides from Alpinia oxyphylla fruit prevent hyperuricemia by inhibiting uric acid synthesis, modulating intestinal flora and reducing renal inflammation.

Hyperuricemia (HUA) is one of the most common chronic diseases today, with a prevalence exceeding 14 % in both the United States and China. Current clinical treatments for HUA focus on promoting uric acid (UA) excretion and inhibiting UA production, but often neglect the strain on the liver and kidneys. The fruit of Alpinia oxyphylla (A.

Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung Cancer treatment revealed by molecular dynamics simulations.

Background: Epidermal growth factor receptor (EGFR) and its signaling pathways play a vital role in pathogenesis of lung cancer. By disturbing EGFR signaling, mutations of EGFR may lead to progression of cancer or the emergence of resistance to EGFR-targeted drugs.

Results: We investigated the correlation between EGFR mutations and EGFR-receptor tyrosine kinase (RTK) crosstalk in the signaling network, in order to uncover the drug resistance mechanism induced by EGFR mutations.

Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer.

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions.

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions.

Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods.

Purpose: Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.

Methods: Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations.