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

Pullulanase is a well-known starch-debranching enzyme. However, the production level of pullulanase is yet low in both wide-type strains and heterologous expression systems. We predicted the disorder propensities of Bacillus naganoensis pullulanase (PUL) using the bioinformatics tool, Disorder Prediction Meta-Server. On the basis of disorder prediction, eight constructs, including PULΔN5, PULΔN22, PULΔN45, PULΔN64, PULΔN78 and PULΔN106 by deleting the first 5, 22, 45, 64, 78 and 106 residues from the N-terminus, and PULΔC9 and PULΔC36 by deleting the last 9 and 36 residues from the C-terminus, were cloned into the recombinant expression vector pET-28a-PelB and auto-induced in Escherichia coli BL21 (DE3) cells. All constructs were evaluated in production level, specific activities and kinetic parameters. Both PULΔN5 and PULΔN106 gave higher production levels of protein than the wide type and displayed increased specific activities. Kinetic studies showed that substrate affinities of the mutants were improved in various degrees and the catalytic efficiency of PULΔN5, PULΔN45, PULΔN78, PULΔN106 and PULΔC9 were enhanced. However, the truncated mutations did not change the advantageous properties of the enzyme involving optimum temperature and pH for further application. Therefore, Disorder prediction-based truncation would be helpful to efficiently improve the enzyme activity and catalytic efficiency.