Structure optimization by heuristic algorithm in a coarse-grained off-lattice model  

作　　者：刘景发 

机构地区：[1]Computer and Software Institute,Nanjing University of Information Science and Technology

出　　处：《Chinese Physics B》2009年第6期2615-2621,共7页中国物理B（英文版）

基　　金：Project supported by the Foundation of Nanjing University of Information Science and Technology;the Excellent Youth Foundation of Education Office of Hunan Province,China (Grant No 07B009)

摘　　要：A heuristic algorithm is presented for a three-dimensional off-lattice AB model consisting of hydrophobic （A） and hydrophilic （B） residues in Fibonacci sequences. By incorporating extra energy contributions into the original potential function, we convert the constrained optimization problem of AB model into an unconstrained optimization problem which can be solved by the gradient method. After the gradient minimization leads to the basins of the local energy minima, the heuristic off-trap strategy and subsequent neighborhood search mechanism axe then proposed to get out of local minima and search for the lower-energy configurations. Furthermore, in order to improve the efficiency of the proposed algorithm, we apply the improved version called the new PERM with importance sampling （nPERMis） of the chain-growth algorithm, pruned-enriched-Rosenbluth method （PERM）, to face-centered-cubic （FCC）-lattice to produce the initial configurations. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we found the ground state energies are lower than the best values reported in the present literature.A heuristic algorithm is presented for a three-dimensional off-lattice AB model consisting of hydrophobic （A） and hydrophilic （B） residues in Fibonacci sequences. By incorporating extra energy contributions into the original potential function, we convert the constrained optimization problem of AB model into an unconstrained optimization problem which can be solved by the gradient method. After the gradient minimization leads to the basins of the local energy minima, the heuristic off-trap strategy and subsequent neighborhood search mechanism axe then proposed to get out of local minima and search for the lower-energy configurations. Furthermore, in order to improve the efficiency of the proposed algorithm, we apply the improved version called the new PERM with importance sampling （nPERMis） of the chain-growth algorithm, pruned-enriched-Rosenbluth method （PERM）, to face-centered-cubic （FCC）-lattice to produce the initial configurations. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we found the ground state energies are lower than the best values reported in the present literature.

关 键 词：protein folding off-lattice model HEURISTICS FCC-lattice 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] Q51[自动化与计算机技术—计算机科学与技术]