title: Disorder prediction-based construct optimization improves activity and catalytic efficiency of Bacillus naganoensis pullulanase.
abstract: 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.
authors.affiliation: School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.
authors.forename: Xinye
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Nie
authors.affiliation: School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Mu
authors.affiliation: School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China.|State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China.|The 2011 Synergetic Innovation Center of Food Safety and Nutrition, Jiangnan University, Wuxi 214122, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ 08854, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Xiao
mesh_terms: D001407:Bacillus / Q000201:enzymology* / Q000235:genetics; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D020134:Catalytic Domain; D006026:Glycoside Hydrolases / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D056004:Molecular Dynamics Simulation; D009154:Mutation
chemical_list: D001426:Bacterial Proteins; D006026:Glycoside Hydrolases; C100181:pullulanase