Application of Combinatorial Optimization Algorithms in the Design of Enzyme Functions

Enzyme function design refers to the process of generating specific functions from wild type through gene mutation, and site-directed mutagenesis is one of the most important ways to change enzyme function. Mathematically enzyme function design through site-directed mutagenesis can be cast as an NP-hard combinatorial optimization problem. In this work, a prediction model is proposed for the optimal combinatorial mutations. At first, saturation mutagenesis data at each active site in the enzyme gene are simulated. Then the effect of combinatorial mutations is predicted on the assumption that the effects at different active sites are independent. Finally, simulated annealing and genetic algorithm are adopted to predict the optimal combinatorial mutations. Simulation results show that both the two optimization algorithms can be used as a candidate solver, but the genetic algorithm performs better. Our prediction model could be used to act as some theoretical guidance for real enzyme function design.