改进的势能曲面变平法在二维非格点模型中的应用

Improved Energy Landscape Paving Method and its Application in 2D Off-lattice Model

蛋白质结构预测问题是生物信息学中的一个重要问题。缺少一种有效的全局寻优方法是阻碍这一问题解决的关键。势能曲面变平(ELP)法是一种启发式的全局优化方法,是一种推广的Monte Carlo方法,已成功地应用于许多优化问题。在ELP法的基础上,提出了改进的势能曲面变平(ELP+)算法。将ELP+算法应用于二维非格点的蛋白质AB模型,预测和发现四条链长分别为13,21,34和55的氨基酸序列的蛋白质结构。数值实验表明,ELP+算法是一种预测蛋白质结构的有效算法。

Protein structure prediction problem is one of the most prominent problems of bioinformatics. Lacking powerful optimization method is the key obstacle to solve this problem. The energy landscape paving （ELP） method is a class of heuristic global optimization algorithm and a generation of Monte Carlo method, and has been successfully applied to solving many optimization problems. Based on the ELP method, an improved energy landscape paving （ELP＋） algorithm is put forward. The ELP＋ algorithm is applied to the 2D off-lattice protein AB model to predict protein structures of four amino acids chains with lengths n= 13,21,34, and 55, respectively. Experimental results show that the ELP＋ algorithm is quite effective in the protein structure prediction problern.