Guo Yuzhen, Wu Zikai, Wang Ying, Wang Yong
Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, People's Republic of China.
Business School, University of Shanghai for Science and Technology, Shanghai, People's Republic of China.
IET Syst Biol. 2016 Feb;10(1):30-3. doi: 10.1049/iet-syb.2015.0059.
In this study, the authors studied the protein structure prediction problem by the two-dimensional hydrophobic-polar model on triangular lattice. Particularly the non-compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem. To solve this problem, classical particle swarm optimisation algorithm was extended by the single point adjustment strategy. Compared with square lattice, conformations on triangular lattice are more flexible in several benchmark examples. They further compared the authors' algorithm with hybrid of hill climbing and genetic algorithm. The results showed that their method was more effective in finding solution with lower energy and less running time.
在本研究中，作者通过二维疏水-极性模型在三角形晶格上研究了蛋白质结构预测问题。特别地，非紧凑构象被建模为将氨基酸序列折叠成一个相对较大的三角形晶格，这比紧凑构象在生物学上更现实且更有意义。然后，蛋白质结构预测问题被抽象为将氨基酸与晶格点进行匹配。在数学上，该问题被表述为一个整数规划问题，并且他们将生物学问题转化为一个优化问题。为了解决这个问题，经典粒子群优化算法通过单点调整策略进行了扩展。与正方形晶格相比，在几个基准示例中，三角形晶格上的构象更灵活。他们进一步将作者的算法与爬山法和遗传算法的混合算法进行了比较。结果表明，他们的方法在找到具有更低能量和更短运行时间的解决方案方面更有效。
