计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 22-26.

• 智能计算 • 上一篇    下一篇

蛋白质构象空间局部增强差分进化搜索方法

董辉,郝小虎,张贵军   

  1. 浙江工业大学信息工程学院 杭州310023,浙江工业大学信息工程学院 杭州310023,浙江工业大学信息工程学院 杭州310023
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61075062,61379020),浙江省自然科学基金(LY13F030008),浙江省科技厅公益项目(2014C33088),浙江省重中之重学科开放基金(20120811),杭州市产学研合作项目(20131631E31)资助

Local Enhancement Differential Evolution Searching Method for Protein Conformational Space

DONG Hui, HAO Xiao-hu and ZHANG Gui-jun   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对蛋白质构象空间搜索问题,提出一种蛋白质构象空间局部增强差分进化搜索方法。在差分进化算法框架下,采用Rosetta Score3粗粒度知识能量模型有效降低构象空间的搜索维数,加快算法收敛速度;引入基于知识的片段组装技术可以有效提高预测精度;利用Monte Carlo算法良好的局部搜索性能对种群做局部增强,以得到更为优良的局部构象;结合差分进化算法较强的全局搜索能力,可以对构象空间进行更为有效的采样。5个测试蛋白实验结果表明,所提算法具有较好的搜索性能和预测精度。

关键词: 蛋白质结构预测,差分进化算法,粗粒度能量模型,片段组装,Monte Carlo

Abstract: A local enhancement differential evolution searching method for protein conformational space was proposed to address the searching problem of protein conformational space.On the framework of differential evolution algorithm,Rosetta Score3 coarse-grained energy model was employed for decreasing the dimension of searching space and improving the convergence rate of algorithm.The knowledge-based fragment assembly technique was introduced for improving the accuracy of prediction.For getting better local near-native conformation,local enhancement operation was done with taking advantage of the well local search performance of Monte Carlo algorithm.The well global searching capacity of the differential evolution algorithm was combined for sampling the whole conformational space effectively.The experi-ment results on 5 test proteins verify the superior searching performance and prediction accuracy of the proposed method.

Key words: Protein structure prediction,Differential evolution algorithm,Coarse-grained energy model,Fragment-assembly,Monte Carlo

[1] 许忠能.生物信息学[M].北京:清华大学出版社,2008
[2] Dill K A,Mac Callum J L.The Protein Folding Problem,50Years on [J].Science 2012,11(338):1042-1046
[3] Anfinsen C.Principles that govern the folding of protein chains [J].Science,1973,181(96):223-230
[4] 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
[5] Kim D E,Blum B,Bradley P,et al.Sampling Bottlenecks in De novo Protein Structure Prediction [J].Journal of molecular bio-logy,2009,393(1):249-260
[6] 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
[7] Hoque M T,Chetty M,Lewis A,et al.Twin removal in geneticalgorithms for protein structure prediction using low-resolution model [J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2011,8(1):234-245
[8] Islam M K,Chetty M.Clustered memetic algorithm with localheuristics for ab initio protein structure prediction [J].IEEE Transactions on Evolutionary Computation,2013,17(4):558-576
[9] 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
[10] Duan Y,Kollman P A.Pathways to a protein folding interme-diate observed in a 1-microsecond simulation in aqueous solution [J].Science,1998,282(5389):740-744
[11] Scheraga H A,Khalili M,Liwo A.Protein folding dynamics:overview of molecular simulation techniques [J].Annu.Rev.Phys.Chem.,2007,58:57-83
[12] Lindorff-Larsen K,Trbovic N,Maragakis P,et al.Structure anddynamics of an unfolded protein examined by molecular dyna-mics simulation[J].Journal of the American Chemical Society,2012,134(8):3787-3791
[13] 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
[14] 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
[15] 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
[16] 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
[17] 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
[18] Joo K,Lee J,Sim S,et al.Protein structure modeling forCASP10 by multiple layers of global optimization [J].Proteins:Structure,Function,and Bioinformatics,2014,82(S2):188-195
[19] Sugita Y,Okamoto Y.Replica-exchange molecular dynamicsmethod for protein folding [J].Chemical Physics Letters,1999,314(1):141-151
[20] Sugita Y,Okamoto Y.Replica-exchange multicanonical algo-rithm and multicanonical replica-exchange method for simulating systems with rough energy landscape [J].Chemical Physics Letters,2000,329(3):261-270
[21] Shehu A.An Ab-initio tree-based exploration to enhance sampling of low-energy protein conformations [C]∥Robotics:Science and Systems.2009:241-248
[22] Shehu A,Olson B.Guiding the search for native-like proteinconformations with an Ab-inito tree-based exploration [J].Robotics Research,2010,29(8):1106-1127
[23] 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
[24] 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
[25] 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
[26] Saleh S,Olson B,Shehu A.A population-based evolutionarysearch approach to the multiple minima problem in de novo protein structure prediction [J].BMC Structural Biology,2013,13(1):1-28
[27] 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
[28] Kortemme T,Morozov A V,Baker D.An orientation- dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes [J].Journal of molecular biology,2003,326(4):1239-1259
[29] 张贵军,郝小虎,周晓根,等.动态步长蛋白质构象空间搜索方法[J].吉林大学学报(工学报)
[30] Huang E S,Samudrala R,Park B H.Scoring functions for ab ini-tio protein structure prediction [M]∥ Protein Structure Prediction:Methods and Protocols.2000
[31] 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
[32] 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
[33] 郝小虎,张贵军,周晓根,等.一种基于片段组装的蛋白质构象空间优化算法[J].计算机科学,2015,42(3):237-240
[34] Storn R,Price K.Differential evolution-a simple and efficientheuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359
[35] Storn R.Differential evolution design of anⅡR-filter [C]∥Proceedings of IEEE International Conference on Evolutionary Computation,1996.Nagoya,1996:268-173
[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] Bradley P,Misura K M,Baker D.Toward high-resolution de novo structure prediction for small proteins [J].Science,2005,309(5742):1868-1871
[38] Zhang Y,Kolinski A,Skolnick J.TOUCHSTONE II:a new approach to ab initio protein structure prediction [J].Biophysics Journal,2003,85(2):1145-1164
[39] Wu S T,Skolnick J,Zhang Y.Ab initio modeling of small proteins by iterative TASSER simulations [J].BMC Biology,2007,5(1):1-10

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