Heterologous expression of nattokinase in E. coli: Biochemical characterization and functional analysis of fibrin binding residues.

Heterologous expression of nattokinase, a potent fibrinolytic enzyme, has been successfully carried out in various microorganisms. However, the successful expression of this enzyme as a soluble protein was not achieved in E. coli. This study delves into the expression of nattokinase in E. coli as a soluble protein followed by its biochemical characterization and functional analysis for fibrinolytic activity. E. coli BL21C41 and pET32a vector host strain with pGro7 protein chaperone induced with IPTG at 16 °C 180 rpm for 16 h enabled the production of recombinant nattokinase in soluble fraction. Enzymatic assays demonstrated its protease activity, while characterization revealed optimal catalytic conditions at 37 °C and pH 8.0, with remarkable stability over a broad pH range (6.0-10.0) and up to 50 °C. The kinetic constants were determined as follows: Km = 25.83 ± 3.43 µM, Vmax = 62.91 ± 1.68 µM/s, kcat = 38.45 ± 1.06 s-1, and kcat/Km = 1.49 × 106 M-1 s-1. In addition, the fibrinolytic activity of NK, quantified by the fibrin plate hydrolysis assay was 1038 ± 156 U/ml, with a corresponding specific activity of 1730 ± 260 U/mg and the assessment of clot lysis time on an artificial clot (1 mg) was found to be 51.5 ± 2.5 min unveiling nattokinase's fibrinolytic potential. Through molecular docking, a substantial binding energy of -6.46 kcal/mol was observed between nattokinase and fibrin, indicative of a high binding affinity. Key fibrin binding residues, including Ser300, Leu302, and Asp303, were identified and confirmed. These mutants affected specifically the fibrin binding and not the proteolytic activity of NK. This comprehensive study provides crucial conditions for the expression of protein in soluble form in E. coli and biochemical properties paving the way for future research and potential applications in medicine and biotechnology.

MSMEG_5850, a global TetR family member supports Mycobacterium smegmatis to survive environmental stress.

A Mycobacterium smegmatis transcriptional regulator, MSMEG_5850, and its ortholog in M. tuberculosis, rv0775 were annotated as putative TetR Family Transcriptional Regulators. Our previous study revealed MSMEG_5850 is involved in global transcriptional regulation in M. smegmatis and the presence of gene product supported the survival of bacteria during nutritional starvation. Phylogenetic analysis showed that MSMEG_5850 diverged early in comparison to its counterparts in virulent strains. Therefore, the expression pattern of MSMEG_5850 and its counterpart, rv0775, was compared during various in-vitro growth and stress conditions. Expression of MSMEG_5850 was induced under different environmental stresses while no change in expression was observed under mid-exponential and stationary phases. No expression of rv0775 was observed under any stress condition tested, while the gene was expressed during the mid-exponential phase that declined in the stationary phase. The effect of MSMEG_5850 on the survival of M. smegmatis under stress conditions and growth pattern was studied using wild type, knockout, and supplemented strain. Deletion of MSMEG_5850 resulted in altered colony morphology, biofilm/pellicle formation, and growth pattern of M. smegmatis. The survival rate of wild-type MSMEG_5850 was higher in comparison to knockout under different environmental stresses. Overall, this study suggested the role of MSMEG_5850 in the growth and adaptation/survival of M. smegmatis under stress conditions.

Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique.

Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models; look at the most popular datasets; and evaluate results in this Literature.

Immobilization and stability studies of a lipase from thermophilic Bacillus sp: The effect of process parameters on immobilization of enzyme

A thermostable lipase was partially purified from the culture supernatant of a thermophilic Bacillus sp. The enzyme is optimally active at 60ºC and pH 8.0. The enzyme showed enhancement in activity in presence of benzene or hexane (30 percent v/v each). The activity (assayed by determining the release of pNP from pNP laurate) was stimulated up to 60 percent of these solvents in enzyme reaction mixture. The catalytic properties of this thermostable enzyme can be further improved via the use of different immobilization techniques and reaction conditions. Enzyme was immobilized on different solid supports and their enzyme activity and stability was compared. The enzyme was adsorbed on silica and HP-20 beads followed by cross-linking with gluteraldehyde on HP-20, which improved the thermostability of enzyme. The optimum pH (pH 8.5) was nearly same for aqueous and immobilized enzyme while optimum temperature was nearly 5ºC higher in case of immobilized enzyme. The immobilized/cross linked enzyme was more thermostable at 70 and 80ºC in comparison to aqueous and surface adsorbed lipase on silica and HP-20. The optimum temperature for esterification reactions was determined to be 60-65ºC. Half-life of immobilized lipase was nearly 2.5 x higher than the aqueous enzyme at 70ºC. Esterification of methanol and oleic acid to methyl oleate by immobilized enzyme was studied in detail.