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基于笛卡尔组合算子的蛋白质折叠问题改进遗传算法

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

作者信息

Rabow A A, Scheraga H A

机构信息

Baker Laboratory of Chemistry, Cornell University, Ithaca, New York 14853-1301, USA.

出版信息

Protein Sci. 1996 Sep;5(9):1800-15. doi: 10.1002/pro.5560050906.

Abstract

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints.

摘要

我们设计了一种笛卡尔组合算子和编码方案,以提高应用于蛋白质折叠问题的遗传算法的性能。遗传编码由蛋白质链的Cα笛卡尔坐标组成。父母基因的重组通过以下方式完成:(1) 将一个亲本链刚性叠加在另一个亲本链上,以使笛卡尔坐标的关系有意义,然后,(2) 通过其父母坐标的线性组合形成子代的链。用这种笛卡尔组合算子方案产生的子代具有相似的拓扑结构,并保留了其父母的长程接触。对于定位蛋白质的低能构象,新方案比标准遗传算法方法显著更有效。还证明了遗传算法相对于蒙特卡罗优化方法的相当大的优越性。我们还设计了一种新的动态规划晶格拟合程序,用于与笛卡尔组合算子方法一起使用。该程序在满足键长、键角和重叠约束的同时,找到了实空间链与晶格的极佳拟合。

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