蛋白质结构从头预测多级个体筛选进化算法

Multi-layer Screening Based Evolution Algorithm for De Novo Protein Structure Prediction

针对蛋白质高维构象空间采样多样性问题,文中提出了一种蛋白质结构从头预测多级个体筛选进化算法(MlISEA)。基于进化算法框架,首先采用基于知识的Rosetta粗粒度能量模型作为优化目标函数,以降低构象空间优化变量维数;其次以基于9片段和3片段的片段组装技术为不同的变异策略,增加同代种群的多样性;同时,设计多级个体筛选方法,进一步增加不同代种群间的多样性;然后利用MonteCarlo算法较强的局部搜索能力对每个个体做局部增强,以得到当前的局部最优解;最后,得到全局最优解以及不同的局部最优解。10个目标蛋白的测试结果表明,所提方法能够有效提高采样多样性,得到TMscore大于0.5的预测构象,为进一步做结构修饰提供便利。

Aiming at the diversity of sampling in high-dimensional protein conformational space,a multi-layer screening based evolution algorithm for de novo protein structure prediction(MlISEA),was proposed.On the basis of the evolution algorithm framework,the knowledge-based Rosetta coarse-grained energy model is employed as the objective function,to reduce the optimal variable dimension of protein conformational space.Taking 9-mer and 3-mer fragment assembly technique as two different kinds of mutation strategies,the diversity of the individuals in the same generation can be increased.In conjunction,multi-layer individual screening method is designed for further improving the diversity of the individuals in different generations.Then,Monte Carlo algorithm is adopted to enhance the performance for each individual to get the local optimal solution.Finally,the global resolution and different local solutions can be obtained.Test results of 10 target proteins show that the proposed method can effectively improve the diversity of sampling,the prediction conformations with TMscore greater than 0.5 can be obtained for further refinement.