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Mengfei Long

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Jul 2026

Rational design of ancestral L-asparaginase via dynamics-guided engineering for improvement of thermostability and acrylamide mitigation efficiency.

L-asparaginase (ASNase) is used both for treating acute lymphoblastic leukemia and as a processing aid to hydrolyze L-asparagine prior to heating, thereby suppressing acrylamide formation in foods and certain traditional Chinese medicinal materials. Industrial pretreatment, however, typically involves high temperature, alkaline conditions, and complex ionic environments, where many enzyme preparations rapidly lose activity, and gains in thermostability often come at the expense of catalytic efficiency. Here, we developed Evolutionary Dynamic Guided Engineering (EDGE), an evolution-and-dynamics-informed strategy that integrates phylogenetic reconstruction with residue-level conformational flexibility profiling to define a target rigidity-flexibility architecture and to select an ancestral starting scaffold. Using EDGE, we performed ancestral sequence reconstruction along the hyperthermophilic branch and identified an inferred ancestral enzyme, designated Anc-ASNase, as the baseline scaffold for engineering. Furthermore, we implemented a two-stage computational pipeline screening, selecting ten single-point candidates for experimental validation. The triple mutant (S282P/A117P/D250M) exhibited a specific activity of 4892.71 U/mg (3.29-fold higher than Anc-ASNase) and a prolonged half-life of 141.6 min at 85 °C. Molecular dynamics simulations indicated that these mutations enhance hydrophobic packing and stabilize interaction networks, suppressing nonproductive fluctuations while retaining functional conformations required for catalysis. Under harsh processing conditions (100 °C, 30 min), the engineered enzyme reduced acrylamide by 70.2% in French fries and by 57.0% in herbal material samples, and it remained tolerant under alkaline conditions and in the presence of several metal ions. Together, these findings support dynamics-guided engineering as a practical route to develop hyperthermophilic ASNase for high-temperature processing and acrylamide mitigation.

Linyu Luo, Shengyu Zhang, Jingbo Zhou et al. · 0 citations
Aug 2026

Computer-aided multi-shell electrostatic remodeling of BhS7Xyl for enhanced activity and Thermostability.

Xylanases with high catalytic efficiency and environmental robustness are important for lignocellulosic biomass valorization, but many enzymes are rapidly inactivated under the alkaline and high-temperature conditions used in industrial processes. In this study, a computationally guided rational-design strategy was developed to improve the catalytic performance and stability of the alkaline xylanase BhS7Xyl. Constant-pH molecular dynamics, isothermal compressibility perturbation analysis, and ECNet-assisted fitness prediction were integrated to identify alkaline-sensitive and structurally unstable residues for engineering. The triple mutant H51R/D150N/E287K showed the best overall performance, with a specific activity of 1045.29 U/mg, representing a 3.73-fold increase compared with the wild type. Its melting temperature increased from 55.82 °C to 64.58 °C, while its half-life at pH 10.0 increased from 33.96 to 95.84 min. The thermal half-life at 75 °C was extended from 10.97 to 215.42 min, corresponding to a 19.64-fold improvement. Structural analyses suggested that the improved performance of H51R/D150N/E287K was associated with a more continuous xylohexaose-binding interface, increased hydrogen-bonding contacts, additional electrostatic/polar interactions, strengthened local interaction networks and enhanced dissipation of local thermal perturbation. Under optimized hydrolysis conditions, the triple mutant produced higher levels of xylose and xylooligosaccharides from standard xylan, corn cob xylan, and hardwood pulp xylan than the wild type. These work demonstrates that multi-shell electrostatic remodeling is a useful strategy for improving the activity, alkaline tolerance, and thermal stability of xylanase for xylooligosaccharide production.

Chun-Lin Tan, Xin Yu, Lanxi Sun et al. · 0 citations

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