Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm

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• 作者：Bai Li ; Mu Lin ; Qiao Liu ; Ya Li ; Changjun Zhou
• 关键词：Artificial bee colony ; Numerical optimization ; Off ; lattice model ; Protein folding ; Protein structure optimization
• 刊名：Journal of Molecular Modeling
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：21
• 期：10
• 全文大小：1,825 KB
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• 作者单位：Bai Li (1) (2)
  Mu Lin (3)
  Qiao Liu (2) (4)
  Ya Li (5)
  Changjun Zhou (6)
  
  1. College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China
  2. School of Advanced Engineering, Beihang University, Beijing, 100191, China
  3. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
  4. School of Instrument Science and Opto-electronic Engineering, Beihang University, Beijing, 100191, China
  5. School of Mathematics and Systems Science & LMIB, Beihang University, Beijing, 100191, China
  6. Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Dalian, 116622, China
  
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Computer Applications in Chemistry
  Biomedicine
  Molecular Medicine
  Health Informatics and Administration
  Life Sciences
  Computer Application in Life Sciences
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：0948-5023

文摘

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.