Protein conformational transitions coupling with ligand interactions: Simulations from molecules to medicine

Oleanolic acid purified from the stem bark of Olax subscorpioidea Oliv. inhibits the function and catalysis of human 17β-hydroxysteroid dehydrogenase 1

Cancer is a leading cause of global death. Medicinal plants have gained increasing attention in cancer drug discovery. In this study, the stem bark extract of Olax subscorpioidea, which is used in ethnomedicine to treat cancer, was subjected to phytochemical investigation leading to the isolation of oleanolic acid (OA). The structure was elucidated by 1-dimensional and 2-dimensional nuclear magnetic resonance spectroscopic (NMR) data, and by comparing its data with previously reported data. Molecular docking was used to investigate the interactions of OA with nine selected cancer-related protein targets. OA docked well with human 17β-hydroxysteroid dehydrogenase type-1 (17βHSD1), caspase-3, and epidermal growth factor receptor tyrosine kinase (binding affinities: -9.8, -9.3, and -9.1 kcal/mol, respectively). OA is a triterpenoid compound with structural similarity to steroids. This similarity with the substrates of 17βHSD1 gives the inhibitor candidate an excellent opportunity to bind to 17βHSD1. The structural and functional dynamics of OA-17βHSD1 were investigated by molecular dynamics simulations at 240 ns. Molecular mechanics/Poisson-Boltzmann surface area (MMPBSA) studies showed that OA had a binding free energy that is comparable with that of vincristine (-52.76, and -63.56 kcal/mol, respectively). The average C-α root mean square of deviation (RMSD) value of OA (1.69 Å) compared with the unbound protein (2.01 Å) indicated its high stability at the protein's active site. The binding energy and stability at the active site of 17βHSD1 recorded in this study indicate that OA exhibited profound inhibitory potential. OA could be a good scaffold for developing new anti-breast cancer drugs.

Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

In the last 20 years, fragment-based drug discovery (FBDD) has become a popular and consolidated approach within the drug discovery pipeline, due to its ability to bring several drug candidates to clinical trials, some of them even being approved and introduced to the market. A class of targets that have proven to be particularly suitable for this method is represented by kinases, as demonstrated by the approval of BRAF inhibitor vemurafenib. Within this wide and diverse set of proteins, protein kinase CK1δ is a particularly interesting target for the treatment of several widespread neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Computational methodologies, such as molecular docking, are already routinely and successfully applied in FBDD campaigns alongside experimental techniques, both in the hit-discovery and in the hit-optimization stage. Concerning this, the open-source software Autogrow, developed by the Durrant lab, is a semi-automated computational protocol that exploits a combination between a genetic algorithm and a molecular docking software for de novo drug design and lead optimization. In the current work, we present and discuss a modified version of the Autogrow code that implements a custom scoring function based on the similarity between the interaction fingerprint of investigated compounds and a crystal reference. To validate its performance, we performed both a de novo and a lead-optimization run (as described in the original publication), evaluating the ability of our fingerprint-based protocol to generate compounds similar to known CK1δ inhibitors based on both the predicted binding mode and the electrostatic and shape similarity in comparison with the standard Autogrow protocol.

Mechanism of interactions between soyasaponins and soybean 7S/11S proteins

In this study, the proteins glycinin (11S) and β-conglycinin (7S) were mixed with soyasaponin (Ssa) Ab/Bb to form a composite system. We used fluorescence and synchronous fluorescence spectra to demonstrate the changes in the surrounding environment and the structure of the proteins. Dynamic interface behavior analysis showed the possible interface behavior induced by the composite system. The interactions between Ssa and the proteins, along with the mode of action, were analyzed by molecular docking. The interactions between Ssa and soy protein increased with the change in concentration. The interactions between the two proteins were mediated by tryptophan (Trp) and primarily involved hydrogen bonds, which changed the microenvironment and loosened the protein structure. These results helped in understanding the mechanism underlying the interactions between Ssa Ab/Bb and 7S/11S. Furthermore, these results highlighted the theoretical fundamentals for the future applications of composite systems as surfactants in the food industry.