In-silico discovery of bifunctional enzymes with enhanced lignocellulose hydrolysis from microbiota big data.

Ariaeenejad S

Ariaeenejad S(1), Kavousi K(2), Mamaghani ASA(3), Motahar SFS(4), Nedaei H(2), Salekdeh GH(5).
Author information:
(1)Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran
(ABRII), Agricultural Research Education and Extension Organization
(AREEO), Karaj, Iran. Electronic address: [Email]
(2)Laboratory of Complex Biological Systems and Bioinformatics
(CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics
(IBB), University of Tehran, Tehran, Iran.
(3)Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran
(ABRII), Agricultural Research Education and Extension Organization
(AREEO), Karaj, Iran.
(4)Department of Food Science and Engineering, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
(5)Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran
(ABRII), Agricultural Research Education and Extension Organization
(AREEO), Karaj, Iran; Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia. Electronic address: [Email]

https://dx.doi.org/10.1016/j.ijbiomac.2021.02.014

Due to the importance of using lignocellulosic biomass, it is always important to find an effective novel enzyme or enzyme cocktail or fusion enzymes. Identification of bifunctional enzymes through a metagenomic approach is an efficient method for converting agricultural residues and a beneficial way to reduce the cost of enzyme cocktail and fusion enzyme production. In this study, a novel stable bifunctional cellulase/xylanase, PersiCelXyn1 was identified from the rumen microbiota by the multi-stage in-silico screening pipeline and computationally assisted methodology. The enzyme exhibited the optimal activity at pH 5 and 50°C. Analyzing the enzyme activity at extreme temperature, pH, long-term storage, and presence of inhibitors and metal ions, confirmed the stability of the bifunctional enzyme under harsh conditions. Hydrolysis of the rice straw by PersiCelXyn1 showed its capability to degrade both cellulose and hemicellulose polymers. Also, the enzyme improved the degradation of various biomass substrates after 168 h of hydrolysis. Our results demonstrated the power of the multi-stage in-silico screening to identify bifunctional enzymes from metagenomic big data for effective bioconversion of lignocellulosic biomass.