Enzyme modification using mutation site prediction method for enhancing the regioselectivity of substrate reaction sites

Enzymes with low regioselectivity of substrate reaction sites may produce multiple products from a single substrate. When a target product is produced industrially using these enzymes, the production of non-target products (byproducts) causes adverse effects such as increased processing costs for purification and the amount of raw material. Thus it is required the development of modified enzymes to reduce the amount of byproducts’ production. In this paper, we report a method called mutation site prediction for enhancing the regioselectivity of substrate reaction sites (MSPER). MSPER takes conformational data for docking poses of an enzyme and a substrate as input and automatically generates a ranked list of mutation sites to destabilize docking poses for byproducts while maintaining those for target products in silico. We applied MSPER to the enzyme cytochrome P450 CYP102A1 (BM3) and the two substrates to enhance the regioselectivity for four target products with different reaction sites. The 13 of the total 14 top-ranked mutation sites predicted by MSPER for the four target products succeeded in selectively enhancing the regioselectivity up to 6.4-fold. The results indicate that MSPER can distinguish differences of substrate structures and the reaction sites, and can accurately predict mutation sites to enhance regioselectivity without selection by directed evolution screening.

A Computational Method to Predict Effects of Residue Mutations on the Catalytic Efficiency of Hydrolases

With scientific and technological advances, growing research has focused on engineering enzymes that acquire enhanced efficiency and activity. Thereinto, computer-based enzyme modification makes up for the time-consuming and labor-intensive experimental methods and plays a significant role. In this study, for the first time, we collected and manually curated a data set for hydrolases mutation, including structural information of enzyme-substrate complexes, mutated sites and Kcat/Km obtained from vitro assay. We further constructed a classification model using the random forest algorithm to predict the effects of residue mutations on catalytic efficiency (increase or decrease) of hydrolases. This method has achieved impressive performance on a blind test set with the area under the receiver operating characteristic curve of 0.86 and the Matthews Correlation Coefficient of 0.659. Our results demonstrate that computational mutagenesis has an instructive effect on enzyme modification, which may expedite the design of engineering hydrolases.

Bioremediation

Pollution is the biggest menace to the living being in this planet today. Enzyme bioremediation is a “breakthrough technology” that holds the potential of pollutant eradication through exploiting the enzyme potential by using the various techniques. Enzyme biocatalysis is referred as white biotechnology and work by green chemistry concept. Moreover, developments in the design and application of enzyme cocktails, mutienzyme complexes, promiscuous enzymes and protein families (cupin and VOC superfamily) has recently emerged a new opportunity in bioremediation. The implementation of various enzyme modification approaches intended for potential bioremediation has been done by adopting enzyme immobilization using magnetic nanoparticles, designer enzymes generation through enzyme engineering, nano-technological advancement for single enzyme nanoparticle generations, electro-bioremediation and carbon nanotube construction. Hence, enzyme bioremediation have greater positive effects and propose significant promise to pollutant bioremediation. In conclusion, the enzymatic bioremediation open the new era of pollutant eradication for clean, safe and green environment.