Highly Efficient Computationally Derived Novel Metagenome α-Amylase With Robust Stability Under Extreme Denaturing Conditions
α-Amylases are among the very critical enzymes used for different industrial purposes. Most α-amylases cannot accomplish the requirement of industrial conditions and easily lose their activity in harsh environments. In this study, a novel α-amylase named PersiAmy1 has been identified through the multistage in silico screening pipeline from the rumen metagenomic data. The long-term storage of PersiAmy1 in low and high temperatures demonstrated 82.13 and 71.01% activities after 36 days of incubation at 4 and 50°C, respectively. The stable α-amylase retained 61.09% of its activity after 180 min of incubation at 90°C and was highly stable in a broad pH range, showing 60.48 and 86.05% activities at pH 4.0 and pH 9.0 after 180 min of incubation, respectively. Also, the enzyme could resist the high-salinity condition and demonstrated 88.81% activity in the presence of 5 M NaCl. PersiAmy1 showed more than 74% activity in the presence of various metal ions. The addition of the detergents, surfactants, and organic solvents did not affect the α-amylase activity considerably. Substrate spectrum analysis showed that PersiAmy1 could act on a wide array of substrates. PersiAmy1 showed high stability in inhibitors and superb activity in downstream conditions, thus useful in detergent and baking industries. Investigating the applicability in detergent formulation, PersiAmy1 showed more than 69% activity after incubation with commercial detergents at different temperatures (30–50°C) and retained more than 56% activity after incubation with commercial detergents for 3 h at 10°C. Furthermore, the results of the wash performance analysis exhibited a good stain removal at 10°C. The power of PersiAmy1 in the bread industry revealed soft, chewable crumbs with improved volume and porosity compared with control. This study highlights the intense power of robust novel PersiAmy1 as a functional bio-additive in many industrial applications.
Frontiers in Microbiology
Digital Object Identifier (DOI)
Ariaeenejad, S., Zolfaghari, B., Sadeghian Motahar, S. F., Kavousi, K., Maleki, M., Roy, S., & Hosseini Salekdeh, G. (2021). Highly Efficient Computationally Derived Novel Metagenome α-Amylase With Robust Stability Under Extreme Denaturing Conditions. Frontiers in microbiology, 12, 713125. https://doi.org/10.3389/fmicb.2021.713125