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 t...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.展开更多
Protein folding problem is one of the most prominent problems of bioinformatics. In this paper, we study a three-dimensional off-lattice protein AB model with two species of monomers, hydrophobic and hydrophilic, and ...Protein folding problem is one of the most prominent problems of bioinformatics. In this paper, we study a three-dimensional off-lattice protein AB model with two species of monomers, hydrophobic and hydrophilic, and present a heuristic quasi-physical algorithm. By elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low-energy configurations for a given monomer chain. A subsequent ＂off-trap＂ strategy is proposed to trigger a jump for a stuck situation in order to get out of local minima. The methods have been tested in the off-lattice AB model. The computational results show promising performance. For all sequences with 13 to 55 monomers, the algorithm finds states with lower energy than previously proposed putative ground states. Furthermore, for the sequences with 21, 34 and 55 monomers, new putative ground states are found, which are different from those given in present literature.展开更多
基金Acknowledgements： The project is supported by the National Natural Science Foundation of China （No. 40471101） and Research Foundation of Nanjing University of Information Science and Technology.
基金Project supported by the Foundation of Nanjing University of Information Science and Technologythe Excellent Youth Foundation of Education Office of Hunan Province,China (Grant No 07B009)
文摘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.
基金This work was supported by the National Grand Fundamental Research 973 Program of China(Grant No.2004CB318000)the National Natural Science Foundation of China nnder Grant No.10471051.
文摘Protein folding problem is one of the most prominent problems of bioinformatics. In this paper, we study a three-dimensional off-lattice protein AB model with two species of monomers, hydrophobic and hydrophilic, and present a heuristic quasi-physical algorithm. By elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low-energy configurations for a given monomer chain. A subsequent ＂off-trap＂ strategy is proposed to trigger a jump for a stuck situation in order to get out of local minima. The methods have been tested in the off-lattice AB model. The computational results show promising performance. For all sequences with 13 to 55 monomers, the algorithm finds states with lower energy than previously proposed putative ground states. Furthermore, for the sequences with 21, 34 and 55 monomers, new putative ground states are found, which are different from those given in present literature.
