Disorder prediction-based construct optimization improves activity and catalytic efficiency of Bacillus naganoensis...

Area of Science:

• Biochemistry
• Enzymology
• Protein Engineering

Background:

• Pullulanase is a key starch-debranching enzyme, but its production levels are often suboptimal in native and recombinant systems.
• Improving pullulanase production and activity is crucial for its industrial applications.

Purpose of the Study:

• To enhance the production and catalytic efficiency of Bacillus naganoensis pullulanase (PUL) through disorder prediction-guided truncation.
• To investigate the impact of N-terminal and C-terminal deletions on PUL's activity, kinetics, and stability.

Main Methods:

• Disorder prediction was used to identify potential truncation sites on PUL.
• Truncated PUL constructs were generated by deleting specific N-terminal and C-terminal residues.
• Recombinant expression in Escherichia coli, followed by evaluation of production levels, specific activities, and kinetic parameters.

Main Results:

• Truncated variants PULΔN5 and PULΔN106 exhibited higher protein production levels compared to the wild type.
• Specific activities were increased in several truncated mutants, notably PULΔN5 and PULΔN106.
• Kinetic studies revealed improved substrate affinities and enhanced catalytic efficiency in mutants like PULΔN5, PULΔN45, PULΔN78, PULΔN106, and PULΔC9.
• Optimal temperature and pH profiles remained largely unaffected by the truncations.

Conclusions:

• Disorder prediction is an effective strategy for identifying truncation sites to improve enzyme properties.
• Truncation of PUL based on disorder prediction significantly enhances enzyme activity and catalytic efficiency.
• This approach provides a valuable method for optimizing pullulanase for biotechnological applications without compromising its operational stability.