Mango peel is a good source of protease but remains an industrial waste. This study focuses on the optimization of polyethylene glycol (PEG)/dextran-based aqueous two-phase system (ATPS) to purify serine protease from mango peel. The activity of serine protease in different phase systems was studied and then the possible relationship between the purification variables, namely polyethylene glycol molecular weight (PEG, 4000-12,000 g·mol(-1)), tie line length (-3.42-35.27%), NaCl (-2.5-11.5%) and pH (4.5-10.5) on the enzymatic properties of purified enzyme was investigated. The most significant effect of PEG was on the efficiency of serine protease purification. Also, there was a significant increase in the partition coefficient with the addition of 4.5% of NaCl to the system. This could be due to the high hydrophobicity of serine protease compared to protein contaminates. The optimum conditions to achieve high partition coefficient (84.2) purification factor (14.37) and yield (97.3%) of serine protease were obtained in the presence of 8000 g·mol(-1) of PEG, 17.2% of tie line length and 4.5% of NaCl at pH 7.5. The enzymatic properties of purified serine protease using PEG/dextran ATPS showed that the enzyme could be purified at a high purification factor and yield with easy scale-up and fast processing.
A thermophilic Bacillus stearothermophilus F1 produces an extremely thermostable serine protease. The F1 protease sequence was used to predict its three-dimensional (3D) structure to provide better insights into the relationship between the protein structure and biological function and to identify opportunities for protein engineering. The final model was evaluated to ensure its accuracy using three independent methods: Procheck, Verify3D, and Errat. The predicted 3D structure of F1 protease was compared with the crystal structure of serine proteases from mesophilic bacteria and archaea, and led to the identification of features that were related to protein stabilization. Higher thermostability correlated with an increased number of residues that were involved in ion pairs or networks of ion pairs. Therefore, the mutants W200R and D58S were designed using site-directed mutagenesis to investigate F1 protease stability. The effects of addition and disruption of ion pair networks on the activity and various stabilities of mutant F1 proteases were compared with those of the wild-type F1 protease.
Amyloid fibers are classified as a new generation of tunable bionanomaterials that exhibit new functions related to their distinctive characteristics, such as their universality, tunability, and stiffness. Here, we introduce the catalytic residues of serine protease into a peptide catalyst (PC) via an enzyme-mimic approach. The rational design of a repeating pattern of polar and nonpolar amino acids favors the conversion of the peptides into amyloid-like fibrils via self-assembly. Distinct fibrous morphologies have been observed at different pH values and temperatures, which indicates that different fibril packing schemes can be designed; hence, fibrillar peptides can be used to generate efficient artificial catalysts for amidolytic activities at mild pH values. The results of atomic force microscopy, Raman spectroscopy, and wide-angle X-ray scattering analyses are used to discuss and compare the fibril structure of a fibrillar PC with its amidolytic activity. The pH of the fibrillation reaction crucially affects the pKa of the side chains of the catalytic triads and is important for stable fibril formation. Temperature is another important parameter that controls the self-assembly of peptides into highly stacked and laminated morphologies. The morphology and stability of fibrils are crucial and represent important factors for demonstrating the capability of the peptides to exert amidolytic activity. The observed amidolytic activity of PC4, one of the PCs, was validated using an inhibition assay, which revealed that PC4 can perform enzyme-like amidolytic catalysis. These results provide insights into the potential use of designed peptides in the generation of efficient artificial enzymes.