Abstract: We elucidated the native structure of a protein molecule from its sequence of amino acids.A problem known as de novo structure prediction,is a long standing challenge in molecular biology.High dimensional conformational space search is the key issue of protein structure prediction that is needed to be solved.Based on differential evolution algorithm framework,we proposed a multimodal protein conformational space optimization algorithm to address the multiple-minima problem in decoy sampling for de novo structure prediction.Algorithm builds the index of similarity measure that is based on the vectors of features of proteins,using exclusion strategy to implement global search.Local minimum search strategy with fragment assembly is able to avoid premature convergence,and can balance the convergence rate and the diversity of the population.A greedy search maps a child conformation to its nearest local minimum,and the molecular fragment replacement technique and differential evolution algorithm help child jump out of local minimum,thus the algorithm can get better search ability.Using Rosetta coarse-grained energy model,results show that the additional mini-mization and the exclusion strategy based on conformation space are key to obtaining a diverse ensemble of decoys.Compared with Baker research team and Shehu research team,the proposed algorithm can achieve better prediction accuracy.

Key words: Multimodal,De novo structure prediction,Crowding differential evolution,Vector of protein structure feature,Fragment replacement