Advances in Microbial Alkaline Proteases: Addressing Industrial Bottlenecks Through Genetic and Enzyme Engineering

Abstract

Microbial alkaline proteases are versatile enzymes chiefly employed in various industrial sectors, viz., food processing, detergents, leather, textile, pharmaceutical industries. However, the existing bottlenecks, such as lower enzyme yields, stability, purification, specificity, and catalytic rates, bring resistance toward their industrial suitability. The robust microbes are prominent sources of stable enzymes. However, further challenges may exist, such as low yield, difficult purification, and lesser enzymatic efficiency. With the advent of advanced genomic and enzyme engineering approaches, such bottlenecks can be overcome. Initially, the microbial genomes can be used as novel repositories for stable enzyme sequences for further heterologous production with higher enzymatic yields and an easier purification process. Moreover, enzyme improvement through directed evolution and rational engineering could enhance enzyme stability and efficiency. Currently, conventional enzyme improvement methods are increasingly replaced by Artificial Intelligence-Machine Learning (AI-ML) and computational data-driven tools that provide precise information for tailoring enzymes for industrial endeavors. Hence, the current review encompasses a deliberate study of microbial alkaline proteases, their major industrial applications, and the bottlenecks in their commercial implementations. Further, it presents in-detailed solutions, including genetic and enzyme engineering, and insights toward incorporating advanced tools like AI-ML and de novo enzyme engineering to subside the existing challenges.

Keywords: AI-ML tools; Alkaline proteases; Bottlenecks; De novo enzyme engineering; Genetic engineering; Industrial applications.