势能曲面变平法的改进及其在三维非格点模型中的应用

An improved energy landscape paving method and its application in 3D off-lattice model

根据蛋白质的氨基酸序列预测其空间结构可以归纳为一个多极值的全局优化问题,缺少一种有效的全局寻优方法是阻碍这一难题解决的一个关键。势能曲面变平(ELP)法是一种启发式的全局优化算法,是一种推广的蒙特卡罗(MC)法,已被成功地应用于蛋白质结构预测问题。本文在ELP法的基础上,提出改进的势能曲面变平(ELP+)算法。将ELP+算法应用于三维非格点的蛋白质AB模型,预测和发现蛋白质结构,数值实验表明ELP+算法是一种预测蛋白质结构的有效算法,计算结果优于ELP和MC算法。

Predicting the structure of a protein from its amino acid sequences is a global optimization problem. Locking powerful optimization method is the key obstacle to this problem. The energy landscape paving （ELP） method is a class of heuristic global optimization algorithm that is a generation of Monte Carlo （MC） method, and has been successfully applied to solving protein structure prediction problem. Based on ELP method, an improved energy landscape paving （ ELP ＋ ） algorithm is put forward. The ELP ＋ algorithm is applied to the 3D off-lattice protein AB model to predict protein structure. Experimental results show that the ELP ＋ algorithm is quite effective in the protein structure prediction problem and outperforms ELP and MC algorithms.