基于鸽子启发优化的蛋白质二级结构预测

Wei Zheng, Hemeng Sun, H. Duan
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引用次数: 3

摘要

蛋白质是所有生物生命过程中必不可少的元素。现有的检测蛋白质结构的实验方法耗时长。根据安芬森的理论,初级结构是形成蛋白质三维结构的关键。因此,避免复杂实验的蛋白质结构预测在理论上是可行的。算法在预测蛋白质结构时存在一些不理想的缺陷。例如,相对较高的吉布斯自由能或较长的迭代过程。本文提出了一种新的算法来解决这一问题。它的名字是“鸽子启发优化(PIO)算法”。PIO可以在相对较短的迭代中做出准确的预测。通过实验,与粒子群算法进行比较,验证了该算法的优越性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protein secondary structure prediction via Pigeon-Inspired Optimization
Proteins are the essential elements in all creatures' life process. The prevailing experimental method to detect protein structure is time consuming. According to Anfinsen's theory, the primary structure is the key to shaping the three-dimensional structure of the protein. Thus, the prediction of proteins' structure avoiding complex experiments is theoretically feasible. Algorithms are applied to make the protein structure prediction while they have some unsatisfactory blemish. For instance, relatively high Gibbs free energy or long iterating progress. In this paper, a new algorithm is introduced to solve this problem. Its name is “Pigeon-Inspired Optimization(PIO) Algorithm”. PIO can work out an accurate prediction in relatively short iterations. The advantages and feasibility of this algorithm will be demonstrated through the experiments, comparing to Particle Swarm Optimization.
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