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.

In Vitro Assessment of The Bioactive Compounds and Anticancer Potential of Citrus medica Leaf Extract.

Citrus medica is a horticultural crop grown in different parts of the world. The plant leaves have medicinal importance in traditional medicine for the treatment of various diseases. The leaves are an underutilised part of the plant, despite having various bioactive compounds with health benefits, with phytochemical analysis having revealed the presence of flavonoids, fatty acids, alkaloids, terpenoids, glycosides, carbohydrates and phytosterols. The biochemical constituents were identified using Fourier-transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC-MS), which confirmed the presence of terpenoids, alcohols, alkanes, phytosterols and fatty acids. Among these, methyl 8, 11, 14-heptadecatrienoate is a linolenic acid, and α-linolenic acid, trimethylsilyl ester and levulinic acid are the predominant compounds belonging to the omega-3 fatty acid group, which has known health benefits. Further, the antimicrobial activity of C. medica plant leaves were tested against certain food-borne pathogens and showed significant results. The minimum inhibitory concentrations ranged from 6.09 mg/mL to 390 mg/mL for bacterial organisms and 48.75 mg/mL to 390 mg/mL for fungal organisms. The antioxidant activity values were 300 µg/mL and 450 µg/mL by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assay, respectively. The methanolic extract from the C. medica leaves also showed anticancer activity against MCF7 breast cancer cell lines, with an IC50 value of material for developing a healthy processed food such as nutraceuticals and functional foods.

Perspectives on Rapid Antigen Tests for Downstream Validation and Development of Theranostics.

Point-of-care SARS-CoV-2 rapid antigen tests have proven to be useful over the years and have become more apparent to the public eye during COVID-19 pandemic due to their ease of use, rapid processing and result times, and low cost. Here, we have assessed the effectiveness and accuracy of rapid antigen tests in comparison to the standard real-time polymerase chain reaction analyses of the same samples.

Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19.

The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted.

Insilico drug repurposing using FDA approved drugs against Membrane protein of SARS-CoV-2.

The novel coronavirus (SARS-CoV-2) outbreak has started taking away the millions of lives worldwide. Identification of known and approved drugs against novel coronavirus disease (COVID-19) seems to be an urgent need for the repurposing of the existing drugs. So, here we examined a safe strategy of using approved drugs of SuperDRUG2 database against modeled membrane protein (M-protein) of SARS-CoV-2 which is essential for virus assembly by using molecular docking-based virtual screening. A total of 3639 drugs from SuperDRUG2 database and additionally 14 potential drugs reported against COVID-19 proteins were selected. Molecular docking analyses revealed that nine drugs can bind the active site of M-protein with desirable molecular interactions. We therefore applied molecular dynamics simulations and binding free energy calculation using MM-PBSA to analyze the stability of the compounds. The complexes of M-protein with the selected drugs were simulated for 50 ns and ranked according to their binding free energies. The binding mode of the drugs with M-protein was analyzed and it was observed that Colchicine, Remdesivir, Bafilomycin A1 from COVID-19 suggested drugs and Temozolomide from SuperDRUG2 database displayed desirable molecular interactions and higher binding affinity towards M-protein. Interestingly, Colchicine was found as the top most binder among tested drugs against M-protein. We therefore additionally identified four Colchicine derivatives which can bind efficiently with M-protein and have better pharmacokinetic properties. We recommend that these drugs can be tested further through in vitro studies against SARS-CoV-2 M-protein.

Antioxidant potential and optimization of production of extracellular polysaccharide by Acinetobacter indicus M6.

BACKGROUND: Extracellular polysaccharides (ECPs) produced by biofilm-producing marine bacterium have great applications in biotechnology, pharmaceutical, food engineering, bioremediation, and bio-hydrometallurgy industries. The ECP-producing strain was identified as Acinetobacter indicus M6 species by 16S rDNA analysis. The polymer produced by the isolate was quantified and purified and chemically analyzed, and antioxidant activities have been studied. The face-centered central composite design (FCCCD) was used to design the model. RESULTS: The results have clearly shown that the ECP was found to be endowed with significant antioxidative activities. The ECP showed 59% of hydroxyl radical scavenging activity at a concentration of 500 µg/mL, superoxide radical scavenging activity (72.4%) at a concentration of 300 µg/mL, and DPPH˙ radical scavenging activity (72.2%) at a concentration of 500 µg/mL, respectively. Further, HPLC and GC-MS results showed that the isolated ECP was a heteropolymer composed of glucose as a major monomer, and mannose and glucosamine were minor monomers. Furthermore, the production of ECP by Acinetobacter indicus M6 was increased through optimization of nutritional variables, namely, glucose, yeast extract, and MgSO4 by "Response Surface Methodology". Moreover the production of ECP reached to 2.21 g/L after the optimization of nutritional variables. The designed model is statistically significant and is indicated by the R2 value of 0.99. The optimized medium improved the production of ECP and is two folds higher in comparison with the basal medium. CONCLUSIONS: Acinetobacter indicus M6 bacterium produces a novel and unique extracellular heteropolysaccharide with highly efficient antioxidant activity. GC-MS analyses elucidated the presence of quite uncommon (1→4)-linked glucose, (1→4)-linked mannose, and (→4)-GlcN-(1→) glycosidic linkages in the backbone. The optimized medium improved the production of ECP and is two folds higher in comparison with the basal medium. The newly optimized medium could be used as a promising alternative for the overproduction of ECP.

Screening of phytochemical compounds of Tinospora cordifolia for their inhibitory activity on SARS-CoV-2: an in silico study.

In the present study, we explored phytochemical constituents of Tinospora cordifolia in terms of its binding affinity targeting the active site pocket of the main protease (3CL pro) of SARS-CoV-2 using molecular docking study and assessed the stability of top docking complex of tinosponone and 3CL pro using molecular dynamics simulations with GROMACS 2020.2 version. Out of 11 curated screened compounds, we found the significant docking score for tinosponone, xanosporic acid, cardiofolioside B, tembetarine and berberine in Tinospora cordifolia. Based on the findings of the docking study, it was confirmed that tinosponone is the potent inhibitor of main protease of SARS-CoV-2 with the best binding affinity of -7.7 kcal/mol. Further, ADME along with toxicity analysis was studied to predict the pharmacokinetics and drug-likeness properties of five top hits compounds. The molecular dynamics simulation analysis confirmed the stability of tinosponone and 3CL pro complex with a random mean square deviation (RMSD) value of 0.1 nm. The computer-aided drug design approach proved that the compound tinosponone from T. cordifolia is a potent inhibitor of 3CL main protease of SARS-CoV-2. Further, the in vitro and in vivo-based testing will be required to confirm its inhibitory effect on SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Design of multi-epitope vaccine candidate against SARS-CoV-2: a in-silico study.

The best therapeutic strategy to find an effective vaccine against SARS-CoV-2 is to explore the target structural protein. In the present study, a novel multi-epitope vaccine is designed using in silico tools that potentially trigger both CD4 and CD8 T-cell immune responses against the novel Coronavirus. The vaccine candidate was designed using B and T-cell epitopes that can act as an immunogen and elicits immune response in the host system. NCBI was used for the retrieval of surface spike glycoprotein, of novel corona virus (SARS-CoV-2) strains. VaxiJen server screens the most important immunogen of all the proteins and IEDB server gives the prediction and analysis of B and T cell epitopes. Final vaccine construct was designed in silico composed of 425 amino acids including the 50S ribosomal protein adjuvant and the construct was computationally validated in terms of antigenicity, allergenicity and stability on considering all critical parameters into consideration. The results subjected to the modeling and docking studies of vaccine were validated. Molecular docking study revealed the protein-protein binding interactions between the vaccine construct and TLR-3 immune receptor. The MD simulations confirmed stability of the binding pose. The immune simulation results showed significant response for immune cells. The findings of the study confirmed that the final vaccine construct of chimeric peptide could able to enhance the immune response against nCoV-19.

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.

Bioactive molecules of probiotic bacteria and their mechanism of action: a review.

The bacteria residing in the gut environment do play a pivotal role in metabolic activities of the host. The metabolites produced by these bacteria affect the physiology and health of the host. The gut bacteria are exposed to environmental conditions where multiple factors such as lifestyle, stress, antibiotics, host genetics and infections have an influence on them. In case of pathogenesis of a disease, the gut bacterial composition is altered which leads to a diseased state. This stage is due to colonization of bacterial pathogens in the gut environment. The pathological condition can be alleviated by administering probiotic strains into the gut environment. The probiotic strains produce therapeutic molecules such as amino acids, vitamins, bacteriocins, enzymes, immunomodulatory compounds and short-chain fatty acids. This review discusses recent evidences of the impact of bioactive molecules produced by probiotic bacteria and their mechanism of action in the gut environment to maintain homeostasis and health of the host without any effect on beneficial bacteria sharing the same niche. In addition, the manufacturing challenges of probiotic products for various applications are discussed here.

Multi level statistical optimization of l-asparaginase from Bacillus subtilis VUVD001.

Physical and chemical factors influencing the anti-leukemic l-asparaginase enzyme production by Bacillus subtilis VUVD001 were optimized using multi-stage optimization on the basis of preliminary experimental outcomes obtained by conventional one-factor-at-a-time approach using shake flasks. Process variables namely carbon, nitrogen sources, pH and temperature were taken into consideration during response surface methodology (RSM) optimization. The finest enzyme activity of 0.51 IUml-1 obtained by OFAT method was enhanced by 3.2 folds using RSM optimization. Artificial neural network (ANN) modelling and genetic algorithm (GA) based optimizations were further carried out to improve the enzyme drug yield. Results were also validated by conducting experiments at optimum conditions determined by RSM and GA optimization methods. The novel bacterium yielded in 2.88 IUml-1 of enzyme activity at optimum process variables determined by GA optimization, i.e., 0.5% glucose, 8.0% beef extract, 8.3 pH and 49.9 °C temperature. The study explored the optimized culture conditions for better yielding of anti-leukemic enzyme drug from a new bacterial source namely Bacillus subtilis VUVD001.

Modeling and optimization of fermentation variables for enhanced production of lactase by isolated Bacillus subtilis strain VUVD001 using artificial neural networking and response surface methodology.

Modeling and optimization were performed to enhance production of lactase through submerged fermentation by Bacillus subtilis VUVD001 using artificial neural networks (ANN) and response surface methodology (RSM). The effect of process parameters namely temperature (°C), pH, and incubation time (h) and their combinational interactions on production was studied in shake flask culture by Box-Behnken design. The model was validated by conducting an experiment at optimized process variables which gave the maximum lactase activity of 91.32 U/ml. Compared to traditional activity, 3.48-folds improved production was obtained after RSM optimization. This study clearly shows that both RSM and ANN models provided desired predictions. However, compared with RSM (R 2 = 0.9496), the ANN model (R 2 = 0.99456) gave a better prediction for the production of lactase.

Optimization of nutritional components of medium by response surface methodology for enhanced production of lactase.

Lactase has excellent applications in dairy industry and commercially this enzyme is produced from bacterial sources but not in high yields. In this work, the production of lactase was improved by designing of nutrient components in fermentation medium by one factor at a time. Lactose and yeast extract were selected as preferable carbon and nitrogen sources for lactase production with tryptophan and MgSO4 showing enhanced production. Statistical analysis proved to be a useful and powerful tool in developing optimum fermentation conditions. The individual and interactive role of lactose, yeast extract, magnesium sulfate, and tryptophan concentration on lactase production was examined by central composite design. Submerged fermentation with Bacillus subtilis strain VUVD001 produced lactase activity of 63.54 U/ml in optimized medium. The activity was threefold higher in comparison to an unoptimized medium. This result confirmed that the designed medium was useful for producing higher yields of lactase.