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

Two new ecdysteroids, spectasterone A (1) and spectasterone B (2), together with four known ecdysteroids, breviflorasterone (3), ajugalactone (4), 20-hydroxyecdysone (5), and polypodine B (6) were isolated from the Korean endemic plant Ajuga spectabilis using feature-based molecular networking analysis. The chemical structures of 1 and 2 were determined based on the interpretation of NMR and mass spectrometric data. Their absolute configurations were established using 3JH, H coupling constants, NOESY interactions, Mosher's method, and ECD and DP4+ calculations. To identify their biological target, a machine learning-based prediction system was applied, and the results indicated that ecdysteroids may have 11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1)-related activity, which was further supported by molecular docking results of ecdysteroids with 11ß-HSD1. Following this result, all the isolated ecdysteroids were tested for their ability to affect the expression of 11ß-hydroxysteroid dehydrogenase type 1 and glucocorticoid receptors (GRs) in HaCaT cells irradiated with UVB. Compounds 2-5 exhibited inhibition of 11ß-HSD1 expression and increases in GR activity.

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. In this study, we demonstrate that minor interactions with low-frequency need to be incorporated to analyze the molecular interactions in the protein-protein interface more efficiently using the complex of SARS-CoV2-RBD and ACE2 receptor as a model system. It was observed that the dominance of interactions in the MD-simulated structures didn't directly correlate with the interactions in the experimentally determined structure. The interactions from the experimentally determined structure could be reproduced better in the ensemble of MD simulated structures by including the less frequent interactions compared to the norm of choosing only highly frequent interactions. Residue Interaction Networks (RINs) analysis also showed that the critical residues in the protein-protein interface could be more efficiently identified by incorporating low-frequency interactions in MD simulation. It is expected that the approach proposed in this study can be a new way of studying protein-protein interaction through MD simulation.

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. In this study, the molecular interactions in the binding interface of mutant Fc and FcγRIIIa were analyzed and compared with those of wild-type Fc binding through residue interaction network (RIN) analysis and molecular dynamics (MD) simulation. RIN analysis identified specific molecular interactions and Hub residues in the interfaces, and their numbers were increased by introducing the mutation, with maintaining most of the molecular interactions in the wild-type complex. MD simulation study revealed that the numbers of stable electrostatic interactions and stable Hub residues in the mutant complex were higher than those in the wild-type complex. The introduced mutations were shown to form further charge-charge attractive interactions in addition to the identified salt bridges without generating any repulsive interactions. These results are expected to provide further structural insight into Fc variants' design based on the S239D/I332E mutation.

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. A clear understanding of how these properties affect the prediction accuracy was observed when these properties were studied combinatorially rather than individually. The combination of low ligand torsion number-hydrophilic ligand-hydrophobic binding site provided the best prediction accuracy while the high ligand torsion number-hydrophilic ligand-hydrophobic binding pocket combination showed the least prediction accuracy. This study allowed us to determine the molecular properties of complex, showing relatively higher or low prediction accuracy and can be employed as a reference in the molecular docking studies using Autodock.

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. To perform such an analysis, the macroscopic molecular properties of the protein and ligand can be studied in detail and should be correlated with the ligand-binding affinity. This detailed study can be helpful in designing the ligands and the ligand-binding site as well. In this study, we attempted to identify the hydrophobic/hydrophilic features of a ligand and ligand-binding site and check their correlation with the experimental affinity of the protein-ligand complexes. This combinatorial analysis of ligand log P and binding site hydrophobicity on data set distribution and binding affinity suggested two critical findings. The hydrophobic ligands bind to hydrophilic and hydrophobic pockets equally, whereas hydrophilic ligands are specific to hydrophilic pockets. The combination of the hydrophobic ligand-hydrophobic pocket prefers high-affinity values compared to other combinations. Although these results cannot be used for atomic-level design of ligands or binding sites, they are expected to be used as a reference for screening the ligands for a given target binding site.