Computational investigation on natural quinazoline alkaloids as potential inhibitors of the main protease (Mpro) of SARS_CoV_2

Drug discovery is still behind in the race compared to vaccine discovery in fighting COVID-19. Recently, a few alkaloids from a traditional Indian medicinal plant, Vasaka (Justicia adhatoda), have been linked computationally to the main protease (Mpro) of SARS_CoV_2. To expand the knowledge and for further investigation, we have selected 41 quinazoline alkaloids from two natural product databases to create an adequate library and performed detailed computational studies against the main protease (Mpro) of SARS_CoV_2. The screening of the library was carried out through blending the rigid docking and pharmacokinetic analysis that resulted in nine alkaloids as initial leads against Mpro. These nine alkaloids were further subjected to advance flexible docking using first reference famotidine for the analysis of structure-based interactions. For further selection, a second screening was carried out based on binding energies and interaction profiles that yielded three alkaloids namely CNP0416047, 3-hydroxy anisotine and anisotine as hits. The stereo-electronic features of hit alkaloids were further investigated through additional structure-based E-pharmacophore mapping against a second reference, known X77 ligand. Additionally, the reactivity of hit alkaloids at the binding site of the protein was estimated by measuring the electron distribution on the frontier molecular orbitals and HOMO-LUMO band energies. Finally, the stabilities of complexes between hit alkaloids with the protein were accessed extensively using robust molecular dynamics simulation through RMSD, RMSF, Rg, and MM-PBSA calculation. Thus, this study identifies three natural quinazoline alkaloids as potential inhibitors of MPro through extensive computational analysis

Real-Time Model-Based Control System Design and Automation for Gelcast Drying Process

Abstract Gelcasting is a new method for manufacturing advanced structural ceramics for use, for example, in the aerospace industry. The process involves a drying stage, where moisture, which constitutes approximately a quarter of the mass of the part, is removed in a commercial dryer. The control system design for the gelcast ceramic drying process is complicated by the requirement of minimizing the drying time while avoiding cracking during shrinkage of the part. This paper describes the application of the Lyapunov theory for finding an exponentially stablizing controller for the nonlinear drying model. A RealSim TRapid Prototyping model and the MATRIXx Tdesign automation environment are used for real-time control, as well as feedback system implementation. It is shown that the proposed design and testing automation process with a user-friendly interactive animation (IA) graphical-user-interface (GUI) provides an effective and efficient environment for real-time control of the gelcast drying process