A novel strategy for partial purification of alkane hydroxylase from P. chrysogenum SNP5 through reconstituting its native membrane into liposome.

Integral proteins or enzymes are still challenging to purify into their native state because of their need for an amphipathic environment and cofactors. Alkane hydroxylase (AlkB) is a membrane-bound enzyme that catalyzes the hydroxylation of a range of alkanes that have a broad spectrum of applications. In the current study, a novel approach has been explored for partial purification of alkane hydroxylase (AlkB) in its native state through restructuring the lipid bilayer of Penicillium chrysogenum SNP5 into a liposome to extend the native and protective environment to AlkB enzyme. Three different methods i.e., reverse-phase evaporation method (RPEM), detergent-based method (DBM), and ethanol injection method (EIM) have been used for reconstituting its native membrane into liposome. On characterizing liposomes through fluorescence imaging, AFM, and particle size analysis, the reverse-phase evaporation method gave the best results based on the size distribution (i.e., 100-300 nm), the morphology of liposomes, and maximum AlkB specific activity (i.e., 140.68 U/mg). The maximum reconstitution efficiency of 29.48% was observed in RPEM followed by 17.3% in DBM and 12.3% in EIM. On the characterization of the purified AlkB, the molecular weight was measured of 44.6 KDa and the thermostability of liposomes synthesized with the RPEM method was obtained maximum at 55 °C. This approach may open a new strategy for the purification of integral enzymes/proteins in their native state in the field of protein purification and its applications in diversified industries.

Enhanced production of alkane hydroxylase from Penicillium chrysogenum SNP5 (MTCC13144) through feed-forward neural network and genetic algorithm.

Alkane hydroxylase (AlkB), a membrane-bound enzyme has high industrial demand; however, its economical production remains challenging due to its intrinsic nature and co-factor dependency. In the current study, various critical process parameters for optimum production of AlkB have been optimized through feed forward neural network (FFNN) and genetic algorithm (GA) models using Penicillium chrysogenum SNP5 (MTCC13144). AlkB specific activity under preliminary un-optimized conditions i.e., 1% hexadecane, 7.4 pH, 11 days incubation time, 28 °C incubation temperature and 1 ml of inoculum size was 100 U/mg. 'One variable at a time' (OVAT) strategy was used to identify optimum physicochemical parameters and then its output data was fed to develop a model of FFNN with '6-12-1' topology. Outputs of FFNN were further optimized through GA to minimize errors and intensify search level. This has provided superior predictive performances with 0.053 U/mg overall mean absolute percentage error (MAPE), 6.801 U/mg root mean square errors (RMSE), and 0.987 overall correlation coefficient (R). The AlkB specific activity improved by 3.5-fold, i.e., from 100 U/mg under preliminary un-optimized conditions to 351.32 U/mg under optimum physicochemical conditions obtained through FFNN-GA hybrid method, i.e., hexadecane (carbon source): 1.56% v/v, FeSO4: 0.63 mM, incubation temperature: 27.40 °C, pH: 7.38, incubation time: 12.35 days and inoculums size: 1.33 ml. The developed process would be a stepping stone to fulfill the high industrial demands of  Alkane hydroxylase.