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Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The AIR is available online at: www.csbio.sjtu.edu.cn\/bioinf\/AIR\/.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz544","type":"journal-article","created":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T15:53:40Z","timestamp":1562255620000},"page":"437-448","source":"Crossref","is-referenced-by-count":25,"title":["Artificial intelligence-based multi-objective optimization protocol for protein structure refinement"],"prefix":"10.1093","volume":"36","author":[{"given":"Di","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition , Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Geng","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition , Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition , Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5720-773X","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Shanghai Jiao Tong University , Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics , Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics , University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4029-3325","authenticated-orcid":false,"given":"Hong-Bin","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition , Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China"},{"name":"Department of Computer Science, Shanghai Jiao Tong University , Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023013112071160500_btz544-B1","doi-asserted-by":"crossref","first-page":"W406","DOI":"10.1093\/nar\/gkw336","article-title":"3Drefine: an interactive web server for efficient protein structure refinement","volume":"44","author":"Bhattacharya","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023013112071160500_btz544-B2","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.compbiolchem.2015.08.006","article-title":"APL: an angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction","volume":"59","author":"Borguesan","year":"2015","journal-title":"Comput. 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