The most important alkaline proteases from the commercial standpoint are produced by bacteria of the genus Bacillus and used mainly in the formulation of detergents. The aim of the present study was to evaluate the production, partial purification, and characteristics of alkaline protease obtained by Bacillus firmus var. arosia NCIB 10557 in fed-batch fermentation with constant feeding profile and carbon source restriction. Firstly, it was carried out on batch fermentation and after 6.5 h of fermentation, glucose became limiting, and then the fed-batch was started with a flow rate of 0.0802 mL/min. Maximum activity (998.1 U/mL) was reached after 10.5 h of fed-batch, with a subsequent 60.91 % drop in activity after two hours. The purification steps resulted in a 1.65-fold increase in the value of the specific activity. The protease showed optimum activity at 37°C and pH 9 and residual activity above 80 % at pH 11 and 12. Residual activity was greater than 70 % at temperatures ranging from 30 to 70 °C and 90 % of this activity was maintained for 30 minutes at 70 °C until the occurrence of complete inactivation. Enzyme activity was estimated using SDS. The organic solvents Triton X-100, Tween-20, EDTA and β-mercaptoethanol and the ions Zn2+, Fe2+, Cu2+ and Ni2+ partially inhibited the activity of the protease. Ca2+, Mn2+ and Mg2+ had no stimulating action on the enzyme.

In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.