Enhancement of α-galactosidase production using novel Actinoplanes utahensis B1 strain: sequential optimization and purification of enzyme.

α-Galactosidase is an important exoglycosidase belonging to the hydrolase class of enzymes, which has therapeutic and industrial potential. It plays a crucial role in hydrolyzing α-1,6 linked terminal galacto-oligosaccharide residues such as melibiose, raffinose, and branched polysaccharides such as galacto-glucomannans and galactomannans. In this study, Actinoplanes utahensis B1 was explored for α-galactosidase production, yield improvement, and activity enhancement by purification. Initially, nine media components were screened using the Plackett-Burman design (PBD). Among these components, sucrose, soya bean flour, and sodium glutamate were identified as the best-supporting nutrients for the highest enzyme secretion by A. Utahensis B1. Later, the Central Composite Design (CCD) was implemented to fine-tune the optimization of these components. Based on sequential statistical optimization methodologies, a significant, 3.64-fold increase in α-galactosidase production, from 16 to 58.37 U/mL was achieved. The enzyme was purified by ultrafiltration-I followed by multimode chromatography and ultrafiltration-II. The purity of the enzyme was confirmed by Sodium Dodecyl Sulphate-Polyacrylamide Agarose Gel Electrophoresis (SDS-PAGE) which revealed a single distinctive band with a molecular weight of approximately 72 kDa. Additionally, it was determined that this process resulted in a 2.03-fold increase in purity. The purified α-galactosidase showed an activity of 2304 U/mL with a specific activity of 288 U/mg. This study demonstrates the isolation of Actinoplanes utahensis B1 and optimization of the process for the α-galactosidase production as well as single-step purification.

Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study.

The aim of this study was to develop an appropriate anti-viral drug against the SARS-CoV-2 virus. An immediately qualifying strategy would be to use existing powerful drugs from various virus treatments. The strategy in virtual screening of antiviral databases for possible therapeutic effect would be to identify promising drug molecules, as there is currently no vaccine or treatment approved against COVID-19. Targeting the main protease (pdb id: 6LU7) is gaining importance in anti-CoV drug design. In this conceptual context, an attempt has been made to suggest an in silico computational relationship between US-FDA approved drugs, plant-derived natural drugs, and Coronavirus main protease (6LU7) protein. The evaluation of results was made based on Glide (Schrödinger) dock score. Out of 62 screened compounds, the best docking scores with the targets were found for compounds: lopinavir, amodiaquine, and theaflavin digallate (TFDG). Molecular dynamic (MD) simulation study was also performed for 20 ns to confirm the stability behaviour of the main protease and inhibitor complexes. The MD simulation study validated the stability of three compounds in the protein binding pocket as potent binders.