Sequence homolog-based molecular engineering for shifting the enzymatic pH optimum

Abstract

Cell-free synthetic biology system organizes multiple enzymes (parts) from different sources to implement unnatural catalytic functions. Highly adaption between the catalytic parts is crucial for building up efficient artificial biosynthetic systems. Protein engineering is a powerful technology to tailor various enzymatic properties including catalytic efficiency, substrate specificity, temperature adaptation and even achieve new catalytic functions. However, altering enzymatic pH optimum still remains a challenging task. In this study, we proposed a novel sequence homolog-based protein engineering strategy for shifting the enzymatic pH optimum based on statistical analyses of sequence-function relationship data of enzyme family. By two statistical procedures, artificial neural networks (ANNs) and least absolute shrinkage and selection operator (Lasso), five amino acids in GH11 xylanase family were identified to be related to the evolution of enzymatic pH optimum. Site-directed mutagenesis of a thermophilic xylanase from Caldicellulosiruptor bescii revealed that four out of five mutations could alter the enzymatic pH optima toward acidic condition without compromising the catalytic activity and thermostability. Combination of the positive mutants resulted in the best mutant M31 that decreased its pH optimum for 1.5 units and showed increased catalytic activity at pH < 5.0 compared to the wild-type enzyme. Structure analysis revealed that all the mutations are distant from the active center, which may be difficult to be identified by conventional rational design strategy. Interestingly, the four mutation sites are clustered at a certain region of the enzyme, suggesting a potential “hot zone” for regulating the pH optima of xylanases. This study provides an efficient method of modulating enzymatic pH optima based on statistical sequence analyses, which can facilitate the design and optimization of suitable catalytic parts for the construction of complicated cell-free synthetic biology systems.

Improvement in catalytic activity and thermostability of a GH10 xylanase and its synergistic degradation of biomass with cellulase

“…Given the complexity of ionic interactions, it is hard to expand the pH activity/adaptability profile of an enzyme through rational design [38]. Until now, most successful cases of shifting the functional pH range were based on random mutagenesis [39–41]. In the present study, the pH activity/stability profiles of XylE were improved by substitution with fragments from XYL10C.…”

Characterisation of the Effect of the Spatial Organisation of Hemicellulases on the Hydrolysis of Plant Biomass Polymer

“…Therefore, the determination of the pH optimum for His -In- Np Xyn11A in presence of BWX was performed, revealing a better activity at pH 6 and 8, while activity at pH 7 was slightly diminished ( Figure S1B ). Actually, the modification of the acid dissociation constant p K a of the catalytic acid is a critical parameter for GH activity and the environment, such as mutation, changing in net charge of the catalytic residue or solvent accessibility could modulate the value of the p K a and cause a shift toward higher pH [ 45 , 46 , 47 , 48 , 49 ]. BWX is much larger and complex compared to p NP-X 3 and fit in both the glycone and aglycone part of the catalytic site, with two negatively charged MeGlcA side chains possibly accommodated by the −3 and/or +2 catalytic subsite [ 50 , 51 ], modifying the environment close to the active site [ 52 ].…”

Bacillus subtilis chassis in biomanufacturing 4.0

“…Consequently, biological reaction constraints should be re‐examined in CFSs, such as the thermodynamic parameters and the optimum pH of enzymes. Fully exploiting thermostable enzymes developed through protein engineering may usher CFS into the new biomanufacturing age 107,108 …”

“…Fully exploiting thermostable enzymes developed through protein engineering may usher CFS into the new biomanufacturing age. 107,108 SYNTHETIC MICROBIAL CONSORTIA SMC are artificial multi-cellular interaction systems that simulate the natural microbial community. Microbial communities often exhibit a population-level phenotype of complexity, relative stability, evolution and fluctuation.…”