Phương pháp lọc và quạt trong mô hình HP hai chiều của bài toán gấp nếp protein

Anfinsen, C. B., Haber, E., Sela, M., & White, F. H. (1961). The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proceedings of the National Academy of Sciences, 47(9), 1309–1314. Backofen, R. (2001). The protein structure prediction problem: a constraint optimization approach using a new lower bound. Constraints, 6, 223–255. Berman, H. M., Westbrook, J., Feng, Z. K., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28(1), 235–242. Bornberg-Bauer, E. (1997). Chain growth algorithms for HP-type lattice proteins. In Proceedings of the first annual international conferences on computational molecular biology (RECOMB97) (pp. 47–55). New York: ACM Press. Chan, H. S., & Dill, K. A. (1993). The protein folding problem. Physics Today, 46(2), 24–32. Chikenji, G., Kiduchi, M., & Iba, Y. (1999). Multi-self-overlap ensemble for protein folding: ground state search and thermodynamics. Physical Review Letters, 83(9), 1886–1889. Covell, D. G., & Jernigan, R. L. (1990). Conformation of folded proteins in restricted spaces. Biochemistry, 29, 3287–3294. Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., & Yanakakis, M. (1998). On the complexity of protein folding. In Proceedings of the 13th annual ACM symposium of theory of computing (STOC 98) (pp. 597–603). Cutello, V., Morelli, G., Nicosia, G., & Pavone, M. (2005). Immune algorithms with aging operators for the string folding problem and the protein folding problem. In Lecture notes in computer sciences (Vol. 3348, pp. 80–90). Berlin: Springer. Cutello, V., Nicosia, G., Pavone, M., & Timmis, J. (2006). An immune algorithm for protein structure prediction on lattice models. IEEE Transaction on Evolutionary Computation. Dandekar, T., & Argos, P. (1994). Folding the main chain of small proteins with genetic algorithm. Journal of Molecular Biology, 236, 844–861. Dill, K. A. (1985). Theory for the folding and stability of globular proteins. Biochemistry, 24(6), 1501–1509. Dongarra, J. J. (2006). Performance of various computers using standard linear equations software (Linpack Benchmark Report). University of Tennessee Computer Science Technical Report, CS-89-85. Glover, F. (1977). Heuristics for integer programming using surrogate constraints. Decision Sciences, 8(1), 156–166. Glover, F. (1989). Tabu search—part I. ORSA Journal on Computing, 1(3), 190–206. Glover, F. (1992). New ejection chain and alternating path methods for traveling salesman problems. Computer Science and Operations Research, 449–509. Glover, F. (1995). Tabu thresholding: improved search by nonmonotonic trajectories. ORSA Journal on Computing, 7(4), 426–442. Glover, F. (1996a). Ejection chains, reference structures and alternating path methods for traveling salesman problems. Discrete Applied Mathematics, 65, 223–253. Glover, F. (1996b). Tabu search and adaptive memory programming—advances, applications and challenges. In Barr, Helgason, & Kennington (Eds.), Interfaces in computer science and operations research (pp. 1–75). Dordrecht: Kluwer Academic. Glover, F. (1998). A template for scatter search and path relinking. In J.-K. Hao, E. Lutton, E. Ronald, M. Schoenauer, & D. Snyers (Eds.), Lecture notes in computer science : Vol. 1363. Artificial evolution (pp. 3–51). Berlin: Springer. Glover, F. (1999). Scatter search and path relinking. In D. Corne, M. Dorigo, & F. Glover (Eds.), New ideas in optimization (pp. 297–316). New York: McGraw–Hill. Glover, F., & Laguna, M. (1993). Tabu search. In C. Reeves (Ed.), Modern heuristic techniques for combinatorial problems (pp. 71–140). Oxford: Blackwell. Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic. Goodman, J., Sokal, A. D. (1986). Multigrid Monte Carlo method for lattice field theories. Physics Review Letters, 56(10), 1015–1018. Grassberger, P. (1997). Pruned-enriched Rosenbluth method: simulations of theta polymers of chain. Physical Review, 56(3), 3682–3693. Hart, W. E., & Istrail, S. (1996). Fast protein folding in the hydrophobic-hydrophilic model within three-eighth of optimal. Journal of Computational Biology, 3(1), 53–96. Hart, W. E., & Istrail, S. (1997). Lattice and off-lattice side chain models of protein folding: linear time structure prediction better than 86% of optimal. Journal of Computational Biology, 4(3), 241–259. Hirst, J. D. (1999). The evolutionary landscape of functional model proteins. Protein Engineering, 12, 721–726. Hsu, H. P., Mehra, V., Nadler, W., & Grassberger, P. (2003a). Growth algorithms for lattice heteropolymers at low temperatures. Journal of Chemical Physics, 118(1), 444–451. Hsu, H. P., Mehra, V., Nadler, W., & Grassberger, P. (2003b). Growth-based optimization algorithm for lattice heteropolymers. Physical Review E, 68(2), 021113. Jiang, T., Cui, Q., Shi, G., & Ma, S. (2003). Protein folding simulations of the hydrophobic-hydrophilic model by combining tabu search with genetic algorithms. Journal of Chemical Physics, 119(8), 4592–4596. Konig, R., & Dandekar, T. (1999). Improving genetic algorithms for protein folding simulation by systematic crossover. BioSystems, 50, 17–25. Krasnogor, N., Pelta, D., Lopez, P. M., Mocciola, P., & de la Canal, E. (1998). Genetic algorithms for the protein folding problem: a critical review. In Proceedings of engineering of intelligence systems (pp. 353–360). ICSC Academic Press. Krasnogor, N., Hart, W. E., Smith, J. E., & Pelta, D. A. (1999). Protein structure prediction with evolutionary algorithms. In Proceedings of the 1999 international genetic and evolutionary computation conference (GECCO99), San Mateo CA (pp. 1596–1601). Krasnogor, N., Blackburnem, B., Pelta, D. A., & Burk, E. K. (2002). Multimeme algorithms for protein structure prediction. In Lecture notes in computer science : Vol. 2439. Proceedings of parallel problem solving from nature (pp. 769–778). Berlin: Springer. Lau, K. F., & Dill, K. A. (1989). A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 2986–3997. Lengauer, T. (1993). Algorithmic research problems in molecular bioinformatics. In Proceedings of the second Israel symposium on theory of computing systems (ISTCS), Natanya, Israel (pp. 177–192). Lesh, N., Mitzenmacher, M., & Whitesides, S. (2003). A complete and effective move set for simple protein folding. In Proceedings of the 7th annual international conference on research in computational molecular biology (RECOMB) (pp. 188–195). New York: ACM Press. Liang, F., & Wong, W. H. (2001). Evolutionary Monte Carlo for protein folding simulations. Journal of Chemical Physics, 115(7), 3374–3380. Nunes, N. J., Chen, K., & Hutchinson (1996). Flexible lattice model to study protein folding. Journal of Physical Chemistry, 100(24), 10443–10449. Pelta, D. A., & Krasnogor, N. (2004). Multimeme algorithms using fuzzy logic based memes for protein structure prediction. In Recent advances in memetic algorithms. Berlin: Springer. Ramakrishnan, R., Ramachandran, B., & Pekny, J. F. (1997). A dynamic Monte Carlo algorithm for exploration of dense conformational spaces in heteropolymers. Journal of Chemical Physics, 106(6), 2418–2424. Rego, C., & Glover, F. (2002). Local search and metaheuristics for the travelling salesman problem. In G. Gutin & A. Punnen (Eds.), The travelling salesman problem and its variations (pp. 309–368). Dordrecht: Kluwer Academic. Richards, F. M. (1991). The protein folding problem. Scientific American, 264(1), 54-7, 60-3. Shmygelska, A., & Hoos, H. H. (2003). An improved ant colony optimization algorithm for the 2D HP protein folding problem. In Lecture notes in computer science. Proceedings of advances in artificial intelligence, AI 2003 (pp. 400–417). Berlin: Springer. Shmygelska, A., & Hoos, H. H. (2005). An ant colony optimization algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics, 6(1), 30. Shmygelska, A., Hernandez, R., & Hoos, H. H. (2002). An ant colony algorithm for the 2D HP protein folding problem. In Lecture notes in computer science : Vol. 2463. Proceedings of the 3rd workshop on ant algorithms (pp. 40–52). Berlin: Springer. Siepmann, J. I., Frenkel, D. (1992). Configurational-bias Monte Carlo: a new sampling scheme for flexible chains. Molecular Physics, 75, 59–70. Skolnick, J., & Kolinski, A. (1990). Simulations of the folding of globular proteins. Science, 250, 1121–1125. Socci, N. D., & Onuchic, J. N. (1994). Folding kinetics of protein like heteropolymers. Journal of Chemical Physics, 101(2), 1519–1528. Unger, R., & Moult, J. (1993). Genetic algorithms for protein folding simulations. Journal of Molecular Biology, 231, 75–81. Zhang, J., Kou, S. C., & Liu, J. S. (2007). Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo. Journal of Chemical Physics, 126, 225101(1)–225101(7).