组合优化算法在酶功能设计中的应用

Application of Combinatorial Optimization Algorithms in the Design of Enzyme Functions

酶功能设计是指野生型酶如何通过基因突变得到具有特定功能的酶的过程,定点突变是改变酶功能的一种重要途径.从数学的观点看,基于定点突变的酶功能设计是一个NP难的组合优化问题.针对该问题,提出一种最优突变组合的预测模型.首先产生酶基因上每个活性位点的饱和突变模拟数据;然后根据位点间的独立性假设得到多个位点组合突变的作用效果;最后利用模拟退火和遗传算法求解最优的突变组合.仿真实验结果表明,遗传算法在求解该问题时具有更优越的性能.文中模型可为生物实验提供一定指导.

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.