Directed evolution of a β-mannanase from Rhizomucor miehei to improve catalytic activity in acidic and thermophilic conditions

Abstract

Backgroundβ-Mannanase randomly cleaves the β-1,4-linked mannan backbone of hemicellulose, which plays the most important role in the enzymatic degradation of mannan. Although the industrial applications of β-mannanase have tremendously expanded in recent years, the wild-type β-mannanases are still defective for some industries. The glycoside hydrolase (GH) family 5 β-mannanase (RmMan5A) from Rhizomucor miehei shows many outstanding properties, such as high specific activity and hydrolysis property. However, owing to the low catalytic activity in acidic and thermophilic conditions, the application of RmMan5A to the biorefinery of mannan biomasses is severely limited.ResultsTo overcome the limitation, RmMan5A was successfully engineered by directed evolution. Through two rounds of screening, a mutated β-mannanase (mRmMan5A) with high catalytic activity in acidic and thermophilic conditions was obtained, and then characterized. The mutant displayed maximal activity at pH 4.5 and 65 °C, corresponding to acidic shift of 2.5 units in optimal pH and increase by 10 °C in optimal temperature. The catalytic efficiencies (k cat/K m) of mRmMan5A towards many mannan substrates were enhanced more than threefold in acidic and thermophilic conditions. Meanwhile, the high specific activity and excellent hydrolysis property of RmMan5A were inherited by the mutant mRmMan5A after directed evolution. According to the result of sequence analysis, three amino acid residues were substituted in mRmMan5A, namely Tyr233His, Lys264Met, and Asn343Ser. To identify the function of each substitution, four site-directed mutations (Tyr233His, Lys264Met, Asn343Ser, and Tyr233His/Lys264Met) were subsequently generated, and the substitutions at Tyr233 and Lys264 were found to be the main reason for the changes of mRmMan5A.ConclusionsThrough directed evolution of RmMan5A, two key amino acid residues that controlled its catalytic efficiency under acidic and thermophilic conditions were identified. Information about the structure–function relationship of GH family 5 β-mannanase was acquired, which could be used for modifying β-mannanases to enhance the feasibility in industrial application, especially in biorefinery process. This is the first report on a β-mannanase from zygomycete engineered by directed evolution.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0833-x) contains supplementary material, which is available to authorized users.

Isolation of Efficient Cellulose Decomposer in Sandy Cropland and Its Application in Straw Turnover in Agro-Pasture Ecotone of Northern China

“…Another species of Rhizomucor (Rhizomucor miehei) could produce lipase, which could be used in dairy products (Soltani et al, 2019) and also in biofuel industry (Rodrigues and Fernandez-Lafuente, 2010;Adnan et al, 2018). Some of the R. miehei could also produce mannose enzyme in strong acid and high-temperature environments (Li et al, 2017). Reports about R. variabilis were mostly focused on medical science.…”

Applications of Microbial β-Mannanases

“…However, the K m values of reAuMan5AY115F and reAuMan5AY111F for guar gum decrease about 47% and 34%, respectively, and their catalytic efficiencies increase 0.7 and 0.5-fold correspondingly as compared with those of parental enzyme, making them excellent candidates for different industrial processes. Li et al (2017b) report that the catalytic efficiencies of a Rhizomucor miehei β-mannanase (RmMan5A) under acidic and thermophilic conditions is enhanced. By using error prone PCR and DNA shuffling a mutant library having 0.72% mutation frequency is created.…”

Deep learning prediction of enzyme optimum pH

“…However, the prohibitively large size and the extremely nonlinear nature of the combinatorial space of possible mutations, coupled with the preponderance of mutations resulting in loss of function or significantly reduced activity, make protein engineering challenging. 16 To overcome these challenges in engineering enzymes with improved pH profiles, researchers have focused on testing a small set of mutations selected to have increased likelihood of success using rational design, 9,[17][18][19][20][21][22] directed evolution, 23,24 and machine learning. 7,25 In recent years, advances in deep learning have led to remarkable improvement in predicting protein properties from amino acid sequence.…”