一种改进的人工蜂群算法用于三维蛋白质结构预测

Ting Li, Changjun Zhou, Mandong Hu
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引用次数: 3

摘要

蛋白质结构预测是生物信息学领域的关键问题之一。热力学假设表明,蛋白质在自然状态下的能量是最低的。因此,通过蛋白质序列的自由能可以直接得到蛋白质的结构。本文提出了一种基于三维AB离格模型的改进算法,以提高人工蜂群算法的局部搜索和全局寻优能力。仿真实验表明,该方法能在保持较高精度的情况下有效地搜索到最低自由能。实验结果表明,改进的人工蜂群算法的最小能量优于其他同类算法,并且随着蛋白质序列长度的增加,该算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved artificial bee colony algorithm for 3D protein structure prediction
One of the key problems in the field of bioinformatics is protein structure prediction. The thermodynamic hypothesis demonstrates that protein's energy is the lowest in nature state. So protein's structure can be gotten directly by protein sequence's free-energy. In this paper, an improved algorithm based on three-dimensional AB off-lattice model to improve local search and global optimization ability of artificial bee colony algorithm has been presented. The simulation experiment shows that it can effectively search the lowest free-energy in the condition of keeping high accuracy. The experimental results indicate that the minimum energy from the improved artificial bee colony algorithm is better than other similar algorithms, and with the increase of protein sequence's length, this algorithm has better performance.
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