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

Shohreh Ariaeenejad*, Kaveh Kavousi, Atefeh Sheykh Abdollahzadeh Mamaghani, Seyedeh Fatemeh Sadeghian Motahar, Hadi Nedaei, Ghasem Hosseini Salekdeh*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)211-220
    Number of pages10
    JournalInternational Journal of Biological Macromolecules
    Publication statusPublished - 30 Apr 2021


    • Bifunctional
    • Cellulase/xylanase
    • In-silico screening
    • Lignocellulosic biomass
    • Metagenomics big data


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