Artificial intelligence-based multi-objective optimization protocol for protein structure refinement

doi:10.1093/bioinformatics/btz544

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	Artificial intelligence-based multi-objective optimization protocol for protein structure refinement
	Wang Di; Geng Ling; Zhao Yu-Jun; Yang Yang; Huang Yan; Zhang Yang; Shen Hong-Bin
刊名	Bioinformatics (Oxford, England)
	2019
卷号	无期号:无页码:无
关键词	无
DOI	10.1093/bioinformatics/btz544
英文摘要	MOTIVATION: Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics method algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS: We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising.
WOS记录号	WOS:无
内容类型	期刊论文
源URL	[http://202.127.2.71:8080/handle/181331/12378]
专题	上海技术物理研究所_上海技物所
推荐引用方式 GB/T 7714	Wang Di,Geng Ling,Zhao Yu-Jun,et al. Artificial intelligence-based multi-objective optimization protocol for protein structure refinement[J]. Bioinformatics (Oxford, England),2019,无(无):无.
APA	Wang Di.,Geng Ling.,Zhao Yu-Jun.,Yang Yang.,Huang Yan.,...&Shen Hong-Bin.(2019).Artificial intelligence-based multi-objective optimization protocol for protein structure refinement.Bioinformatics (Oxford, England),无(无),无.
MLA	Wang Di,et al."Artificial intelligence-based multi-objective optimization protocol for protein structure refinement".Bioinformatics (Oxford, England) 无.无(2019):无.