Abstract: Due to the inaccuracy of protein energy model,the optimal solution in mathematics does not consistently correspond to the stable natural structure of a given protein.The existing methods tend to converge to local optimal solutions because of the huge conformational space.To address the problem of the inaccuracy of energy model in the field of protein structure prediction and the reliability of high-dimensional conformational space sampling,a dihedral angle similarity based multimodal conformation optimization method (DASOM) for ab-initio protein structure prediction is proposed in the framework of population based algorithm.Firstly,the modal exploration is conducted,knowledge-based Rosetta coarse-grained energy model is used as the standard to select new individuals with high quality,thus the diversity of the population can be increased.Then,a dihedral angular similarity model is established to meet the requirements of similar individual determination in the multi-modal optimization algorithm.Crowding update strategy is used for optimizing the existing modal to achieve the modal enhancement and more reasonable conformation is obtained.Experimental results on 10 test proteins show that the proposed method not only achieves high prediction accuracy,but also obtains many metastable protein conformations as well.

Key words: Coarse-grained energy model, Dihedral angular similarity model, Multimodal optimization, Protein structure prediction, Rosetta