caa a22 4500 605512337 CHVBK 20210128100655.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00894-015-2806-y doi (NATIONALLICENCE)springer-10.1007/s00894-015-2806-y Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm [Elektronische Daten] [Bai Li, Mu Lin, Qiao Liu, Ya Li, Changjun Zhou] Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques Springer-Verlag Berlin Heidelberg, 2015 Artificial bee colony nationallicence Numerical optimization nationallicence Off-lattice model nationallicence Protein folding nationallicence Protein structure optimization nationallicence Li Bai College of Control Science and Engineering, Zhejiang University, 310027, Hangzhou, China aut Lin Mu College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027, Hangzhou, China aut Liu Qiao School of Advanced Engineering, Beihang University, 100191, Beijing, China aut Li Ya School of Mathematics and Systems Science & LMIB, Beihang University, 100191, Beijing, China aut Zhou Changjun Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, 116622, Dalian, China aut Journal of Molecular Modeling Springer Berlin Heidelberg 21/10(2015-10-01), 1-15 1610-2940 21:10<1 2015 21 894 https://doi.org/10.1007/s00894-015-2806-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00894-015-2806-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Bai College of Control Science and Engineering, Zhejiang University, 310027, Hangzhou, China aut NATIONALLICENCE 700 1- Lin Mu College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027, Hangzhou, China aut NATIONALLICENCE 700 1- Liu Qiao School of Advanced Engineering, Beihang University, 100191, Beijing, China aut NATIONALLICENCE 700 1- Li Ya School of Mathematics and Systems Science & LMIB, Beihang University, 100191, Beijing, China aut NATIONALLICENCE 700 1- Zhou Changjun Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, 116622, Dalian, China aut NATIONALLICENCE 773 0- Journal of Molecular Modeling Springer Berlin Heidelberg 21/10(2015-10-01), 1-15 1610-2940 21:10<1 2015 21 894