Biotechnological Applications of Serine Proteases: A Patent Review

Identification and characterization of a serine protease from Bacillus licheniformis W10: A potential antifungal agent

Bacillus licheniformis W10 is a strain of biocontrol bacteria that was obtained from plant rhizosphere screening. In this study, we purified, identified, and carried out bioinformatics analysis of the W10 antifungal protein from Bacillus licheniformis. Mass spectrometry analysis was carried out by passing the antifungal protein through a high-resolution time-of-flight mass spectrometer. Mascot searches of the tandem mass spectrometry data identified this antifungal protein as a serine protease, and the 1347 bp gene encoding this protein was cloned. Bioinformatics analysis of this protein indicated that it contains 448 amino acid residues, has a molecular weight of 48,794.16 Da and an isoelectric point of 6.04, and is a hydrophilic protein. In the secondary and tertiary structure of this protein, the proportion of α-helices and β-folds is similar, and the protein possesses a Peptidase_S8 conserved domain. Using BApNA as a substrate, it was found that the serine protease inhibitor phenylmethylsulfonyl fluoride (PMSF) can inhibit the W10 antifungal protein. PMSF concurrently reduced the inhibitory effects of the antifungal protein on Botrytis cinerea, showing that the W10 antifungal protein possesses serine protease activity. The W10 antifungal protein has good thermal stability. The study implies potential of this enzyme for biocontrol of fungal plant pathogens.

iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC

Proteases are a type of enzymes, which perform the process of proteolysis. Proteolysis normally refers to protein and peptide degradation which is crucial for the survival, growth and wellbeing of a cell. Moreover, proteases have a strong association with therapeutics and drug development. The proteases are classified into five different types according to their nature and physiochemical characteristics. Mostly the methods used to differentiate protease from other proteins and identify their class requires a clinical test which is usually time-consuming and operator dependent. Herein, we report a classifier named iProtease-PseAAC (2L) for identifying proteases and their classes. The predictor is developed employing the flow of 5-step rule, initiating from the collection of benchmark dataset and terminating at the development of predictor. Rigorous verification and validation tests are performed and metrics are collected to calculate the authenticity of the trained model. The self-consistency validation gives the 98.32% accuracy, for cross-validation the accuracy is 90.71% and jackknife gives 96.07% accuracy. The average accuracy for level-2 i.e. protease classification is 95.77%. Based on the above-mentioned results, it is concluded that iProtease-PseAAC (2L) has the great ability to identify the proteases and their classes using a given protein sequence.