Adaptability to the environment of protease by secondary structure changes and application to enzyme-selective hydrolysis.

The reactions involving enzymes are significantly influenced by various environmental factors. Clarity of how the activity and structure of proteases impact their function is crucial for more efficient application of enzymes as a tool. The impact of temperature, pH, and ionic strength on changes in protease activity, secondary structure, and protein conformation during enzymatic hydrolysis were investigated in this study. The enzymatic activity and secondary structure of acid-base protease were found to undergo significant modifications under different physical conditions, as demonstrated by UV spectrophotometry and FTIR spectroscopy analysis. Specifically, variations in α-helix and ß-fold content were observed to correlate with changes in enzyme activity. Molecular simulation analysis revealed that physical conditions have varying effects on the protease, particularly influencing enzyme activity and secondary structure. Evaluation of the proteases indicated alterations in both enzyme activity and structure. This treatment selectively hydrolyzed ß-lactoglobulin and reduced sensitization. These findings offer novel perspectives on the functionalities and regulatory mechanisms of proteases, as well as potential industrial applications.

Rapid and accurate quantification of trypsin activity using integrated infrared and ultraviolet spectroscopy with data fusion techniques.

Proteases play a crucial role in industrial enzyme formulations, with activity fluctuations significantly impacting product quality and yield. Therefore, developing a method for precise and rapid detection of protease activity is paramount. This study aimed to develop a rapid and accurate method for quantifying trypsin activity using integrated infrared (IR) and ultraviolet (UV) spectroscopy combined with data fusion techniques. The developed method evaluates the enzymatic activity of trypsin under varying conditions, including temperature, pH, and ionic strength. By comparing different data fusion methods, the study identifies the optimal model for accurate enzyme activity prediction. The results demonstrated significant improvements in predictive performance using the feature-level data fusion approach. Additionally, substituting the spectral data of the samples in the validation sets into the best prediction model resulted in a minimal residual difference between predicted and true values, further verifying the model's accuracy and reliability. This innovative approach offers a practical solution for the efficient and precise quantification of enzyme activity, with broad applications in industrial processes.