Ecdysteroids from the Korean Endemic Species with Activities against Glucocorticoid Receptors and 11β-Hydroxysteroid Dehydrogenase Type 1.

Two new ecdysteroids, spectasterone A () and spectasterone B (), together with four known ecdysteroids, breviflorasterone (), ajugalactone (), 20-hydroxyecdysone (), and polypodine B () were isolated from the Korean endemic plant using feature-based molecular networking analysis. The chemical structures of and were determined based on the interpretation of NMR and mass spectrometric data. Their absolute configurations were established using coupling constants, NOESY interactions, Mosher's method, and ECD and DP4+ calculations.

Analysis of protein-protein interface with incorporating low-frequency molecular interactions in molecular dynamics simulation.

Protein-protein interactions are vital for various biological processes such as immune reaction, signal transduction, and viral infection. Molecular Dynamics (MD) simulation is a powerful tool for analyzing non-covalent interactions between two protein molecules. In general, MD simulation studies on the protein-protein interface have focused on the analysis of major and frequent molecular interactions.

Residue interaction network and molecular dynamics simulation study on the binding of S239D/I332E Fc variant with enhanced affinity to FcγRIIIa receptor.

Engineering of Fc has been adapted as an efficient method for enhanced or reduced affinity towards Fc receptors in the development of therapeutic antibodies. S239D/I332E mutation of Fc induces approximately two logs greater affinity to the FcγRIIIa receptor and has been extensively employed in various Fc engineering studies. It is known that the mutation gives rise to the formation of salt bridges between the mutated residues of Fc and FcγRIIIa, but the overall effect of the mutation in the binding interface of the Fc-FcγRIIIa complex is still unclear.

Effect of molecular properties of the protein-ligand complex on the prediction accuracy of AutoDock.

Molecular docking approach has been extensively used to predict the ligand's binding conformation in the binding pocket of protein. However, its prediction accuracy is still limited and highly dependent on target protein-ligand complexes. In this study, we investigated the effects of ligand torsion number, ligand hydrophobicity, and binding-site hydrophobicity on the prediction accuracy of Autodock, a popular molecular docking tool, combinatorially as well as respectively.

Combinatorial Effect of Ligand and Ligand-Binding Site Hydrophobicities on Binding Affinity.

Computational methods to study protein-ligand interactions at a molecular level have been successful to a certain extent in predicting the pose, atomic interactions, and so forth, but poor efficiency in estimating a protein-ligand binding affinity is still a crucial problem to be solved. Analyzing the protein-ligand interactions quantitatively is one primary concern for understanding. Qualitative analysis of these interactions may lead to better insights about protein-ligand interactions.