吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 585-594.doi: 10.13229/j.cnki.jdxbgxb201602037
• Orginal Article • Previous Articles Next Articles
ZHANG Gui-jun, HAO Xiao-hu, ZHOU Xiao-gen, QIN Chuan-qing
CLC Number:
| [1] Ken A Dill, Justin L, Mac Callum. The protein folding problem, 50 years on[J]. Science, 2012, 338 (11): 1042-1046. [2] Anfinsen C. Principles that govern the folding of protein chains[J]. Science, 1973, 181(96): 223-230. [3] 许忠能.生物信息学[M].北京:清华大学出版社,2008. [4] Kim D E, Blum B, Bradley P, et al. Sampling bottlenecks in de novo protein structure prediction[J]. Journal of Molecular Biology, 2009, 393(1): 249-260. [5] 黄俊峰,段鹏,吴文言.基于模板的蛋白质结构预测[J].生物物理学报,2011,27(1):28-37. Huang Jun-feng, Duan Peng, Wu Wen-yan. Protein structure prediction based on the template[J]. Acta Biophysica Sinica, 2011,27(1):28-37. [6] Kryshtafovych A, Fidelis K, Moult J. CASP10 results compared to those of previous CASP experiments[J]. Proteins: Structure, Function, and Bioinformatics, 2014, 82(S2): 164-174. [7] Beliakov G, Lim K F. Challenges of continuous global optimization in molecular structure prediction[J]. European Journal of Operational Research, 2007, 181(3): 1198-1213. [8] Tantar A A, Melab N, Talbi E G, et al. A parallel hybrid genetic algorithm for protein structure prediction on the computational grid[J]. Future Generation Computer Systems, 2007, 23(3): 398-409. [9] Hoque M T, Chetty M, Lewis A, et al. Twin removal in genetic algorithms for protein structure prediction using low-resolution model[J]. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 2011, 8(1): 234-245. [10] Islam M K, Chetty M. Clustered memetic algorithm with local heuristics for Ab-initio protein structure prediction[J]. IEEE Transactions on Evolutionary Computation, 2013, 17(4): 558-576. [11] Custódio F L, Barbosa H J C, Dardenne L E. A multiple minima genetic algorithm for protein structure prediction[J]. Applied Soft Computing, 2014, 15: 88-99. [12] Duan Y, Kollman P A. Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution[J]. Science, 1998, 282(5389): 740-744. [13] Scheraga H A, Khalili M, Liwo A. Protein folding dynamics: overview of molecular simulation techniques[J]. Annu Rev Phys Chem, 2007, 58: 57-83. [14] Lindorff-Larsen K, Trbovic N, Maragakis P, et al. Structure and dynamics of an unfolded protein examined by molecular dynamics simulation[J]. Journal of the American Chemical Society, 2012, 134(8): 3787-3791. [15] Zhang Y, Kihara D, Skolnick J. Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding[J]. Proteins: Structure, Function, and Bioinformatics, 2002, 48(2): 192-201. [16] Shen Y, Picord G, Guyon F, et al. Detecting protein candidate fragments using a structural alphabet profile comparison approach[J]. PloS One, 2013, 8(11): e80493. [17] Xu D, Zhang Y. Toward optimal fragment generations for ab-initio protein structure assembly[J]. Proteins: Structure, Function, and Bioinformatics, 2013, 81(2): 229-239. [18] Dotu I, Cebrian M, Van Hentenryck P, et al. On lattice protein structure prediction revisited[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011, 8(6): 1620-1632. [19] Tyka M D, Jung K, Baker D. Efficient sampling of protein conformational space using fast loop building and batch minimization on highly parallel computers[J]. Journal of Computational Chemistry, 2012, 33(31): 2483-2491. [20] Joo K, Lee J, Sim S, et al. Protein structure modeling for CASP10 by multiple layers of global optimization[J]. Proteins: Structure, Function, and Bioinformatics, 2014, 82(S2): 188-195. [21] Sugita Y, Okamoto Y. Replicae-xchange molecular dynamics method for protein folding[J]. Chemical Physics Letters, 1999, 314(1): 141-151. [22] Sugita Y, Okamoto Y. Replica-exchange multicanonical algorithm and multicanonical replica-exchange method for simulating systems with rough energy landscape[J]. Chemical Physics Letters, 2000, 329(3): 261-270. [23] Shehu A. An Ab-initio tree-based exploration to enhance sampling of low-energy protein conformations[C]∥Robotics: Science and Systems,2009: 241-248. [24] Shehu A, Olson B. Guiding the search for native-like protein conformations with an Ab-inito tree-based exploration[J]. Robotics Research,2010, 29(8):1106-1127. [25] Olson B, Molloy K, Shehu A. In search of the protein native state with a probabilistic sampling approach[J]. Journal of Bioinformatics and Computational Biology, 2011, 9(3): 383-398. [26] Olson B, Shehu A. Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface[J]. Proteome Sci, 2012, 10(Suppl 1): S5. [27] Molloy K, Saleh S, Shehu A. Probabilistic search and energy guidance for biased decoy sampling in Ab initio protein structure prediction[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 2013, 10(5): 1162-1175. [28] 周建红,艾观华,方慧生,等.蛋白质结构从头预测方法研究进展[J].生物信息学, 2011,9(1):1-5. Zhou Jian-hong, Ai Guan-hua, Fang Hui-sheng, et al. Progress in ab initio protein structure prediction[J]. Bioinformatics, 2011, 9(1):1-5. [29] Bradley P, Misura K M, Baker D. Toward high-resolution de novo structure prediction for small proteins[J]. Science, 2005, 309(5742): 1868-1871. [30] Liwo A, Khalili M, Scherage H A. Ab-initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains[J]. PNAS, 2005, 102 (7): 2362-2367. [31] Kuhlman B, Baker D. Native protein sequences are close to optimal for their structures[J]. Proceedings of the National Academy of Sciences of the Unitized States of America, 2000, 97(19): 10383-10388. [32] Kortemme T, Morozov A V, Baker D. An orientation-dependent hydrogen bonding potential improves prediction of speci?city and structure for proteins and protein-protein complexes[J]. Journal of Molecular Biology, 2003, 326(4): 1239-1259. [33] Enoch S Huang, Ram Samudrala, Britt H Park. Scoring functions for ab initio protein structure prediction[J]. Methods Mol Biol, 2000, 143 (1): 223-245. [34] Ballester P J, Richards W G. Ultrafast shape recognition for similarity search in molecular databases[J]. Proc R Soc A, 2007,463(2081): 1307-1321. [35] Lee J, Scheraga H A, Rackovsky S. New optimization method for conformational energy calculations on polypeptides: conformational space annealing[J]. Journal of Computational Chemistry, 1997, 18(9): 1222-1232. [36] Berg B A, Neuhaus T. Multicanonical ensemble: a new approach to simulate first-order phase transitions[J]. Physical Review Letters, 1992, 68(1):9-12. [37] Wang G, Dunbrack R L. PISCES: a protein sequence culling server[J]. Bioinformatics, 2003, 19(12): 1589-1591. [38] Gront D, Kulp D W, Vernon R M, et al. Generalized fragment picking in Rosetta: design, protocols and applications[J]. PLoS One, 2011, 6(8): e23294. [39] Keaver-Fay A, Tyka M, Lewis S M, et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules[J]. Methods in Enzymology, 2011, 487:545-574. [40] Li Z, Scheraga H A. Monte Carlo-minimization approach to the multiple-minima problem in protein folding[J]. PNAS, 1987, 84(19): 6611-6615. [41] Lee J Y, Lee J Y, Sasaki T N, et al. De novo protein structure prediction by dynamic fragment assembly and conformational space annealing[J]. Proteins, 2011, 79(8):2403-2417. [42] Stoean C, Preuss M, Stoean R, et al. Multimodal optimization by means of a topological species conservation algorithm[J]. Evolutionary Computation, 2010, 14(6): 842-864. |
| [1] | DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577. |
| [2] | GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585. |
| [3] | WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205. |
| [4] | ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213. |
| [5] | HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865. |
| [6] | LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873. |
| [7] | LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881. |
| [8] | WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532. |
| [9] | YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538. |
| [10] | LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544. |
| [11] | QU Hui-yan, ZHAO Wei, QIN Ai-hong. A fast collision detection algorithm based on optimization operator [J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603. |
| [12] | LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611. |
| [13] | SHAO Ke-yong, CHEN Feng, WANG Ting-ting, WANG Ji-chi, ZHOU Li-peng. Full state based adaptive control of fractional order chaotic system without equilibrium point [J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230. |
| [14] | WANG Sheng-sheng, WANG Chuang-feng, GU Fang-ming. Spatio-temporal reasoning for OPRA direction relation network [J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243. |
| [15] | MA Miao, LI Yi-bin. Multi-level image sequences and convolutional neural networks based human action recognition method [J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252. |
|