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Differential Evolution Explores a Multiobjective Knowledge-based Energy Function for Protein Structure Prediction

陳 星倩 富山大学

2022.03.23

概要

Proteins are complex large organic macromolecules that are composed of one or more chains of 20 different amino acids in specific orders. Many fundamental biological functions in organisms are performed by proteins, such as structural support, material transport, and regulation functions. Since the structure of a protein determines its biological functions, knowledge of its native structures is essential for understanding its role in life activities. Three experimental methods, i.e., X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy, are commonly used to perform protein structure determination. However, all of these experimental methods are costly and waste much of time. On the other hand, since the three-dimensional structure of a protein is determined by its amino acid sequence, it is very meaningful for researchers to obtain the three-dimensional (3D) structure of the protein from its sequence by calculation methods.

How to predict the 3D structure of a given protein starting only from its amino acid sequence is called the protein structure prediction (PSP) problem. The high theoretical value and practical significance make the research of this problem necessary and promising. Despite the rapid development of computer techniques and the unremitting efforts of researchers, the PSP problem remains challenging in bioinformatics and computational biology. Numerous approaches have been proposed to solve the PSP problem. These approaches can be roughly grouped into two categories: template-based modeling and free modeling (FM). The two pivotal factors of a successful FM prediction approach are an efficient search strategy and an effective energy function.

Since the conformation search space is very large, an exhaustive search strategy is infeasible under normal circumstances. A successful FM must employee efficient search strategies to find the global minimum of a given energy function. The most common conformation search method employed in FMs is the Monte Carlo algorithm or its variations. Recently, employing evolutionary computation techniques as the search strategy in FMs has attracted researchers' interest, and considerable success has been achieved. Protein energy functions are used to select more native-like conformations during the process of protein folding. The existing protein energy functions can be roughly classified into two groups: physics-based energy functions and knowledge-based energy functions.

In my research of defending PhD, I try to model the PSP problem as a multi-objective optimization problem and use an differential evolution search strategy to solve the problem. In details, the PSP problem is modeled as a multiobjective optimization problem, and a FM approach called MODE-K is proposed to solve this problem. my efforts center on two aspects. First, a knowledge-based energy function called RWplus is used as the evaluation criterion. This function is decomposed into two terms: an orientation-dependent energy term and a distance-dependent energy term. Second, a multiobjective differential evolution coupled with an external archive employed to perform conformation space searching. After conformation space searching, we introduce a cluster method to select the final predicted structure from series of decoy structures. The performance of the method was verified with eighteen test proteins. The experimental results demonstrate the effectiveness of the proposed method and indicate that incorporating knowledge-based energy functions into multiobjective approaches to solve the PSP problem is promising.

The contribution of this thesis is fourfold: first, the PSP problem is modeled as a multiobjective optimization problem and two knowledge-based energy terms are used to construct the energy function. Second, a new MODE algorithm that interacts with an external archive is proposed. Third, an integral work flow is provided. The clustering method which called MUFOLD-CL is used to identify the final predicted structure from a set of decoy structures that are stored in the archive. Fourth, eighteen test proteins categorized into three structural classes are used to evaluate the proposed method. More investigation of the experimental results provides evidence of the superior performance of the proposed approach.

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参考文献

[1] Johannes S¨oding. Big-data approaches to protein structure prediction. Science, 355(6322):248–249, 2017.

[2] Fred E Cohen and Jeffery W Kelly. Therapeutic approaches to proteinmisfolding diseases. Nature, 426(6968):905, 2003.

[3] Yang Zhang. Protein structure prediction: when is it useful? Current opinion in structural biology, 19(2):145–155, 2009.

[4] Xiaogen Zhou, Chun-Xiang Peng, Jun Liu, Yang Zhang, and Gui-jun Zhang. Underestimation-assisted global-local cooperative differential evolution and the application to protein structure prediction. IEEE Transactions on Evolutionary Computation, 2019.

[5] Ken A Dill and Justin L MacCallum. The protein-folding problem, 50 years on. science, 338(6110):1042–1046, 2012.

[6] Christian B. Anfinsen. Principles that govern the folding of protein chains. Science, 181(4096):223–230, 1973.

[7] John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede, and Anna Tramontano. Critical assessment of methods of protein structure prediction (casp)—round xii. Proteins: Structure, Function, and Bioinformatics, 86:7–15, 2018.

[8] Stephen K Burley, Helen M Berman, Cole Christie, Jose M Duarte, Zukang Feng, John Westbrook, Jasmine Young, and Christine Zardecki. Rcsb protein data bank: Sustaining a living digital data resource that enables breakthroughs in scientific research and biomedical education. Protein Science, 27(1):316–330, 2018.

[9] Ambrish Roy, Alper Kucukural, and Yang Zhang. I-tasser: a unified platform for automated protein structure and function prediction. Nature protocols, 5(4):725, 2010.

[10] Marco Biasini, Stefan Bienert, Andrew Waterhouse, Konstantin Arnold, Gabriel Studer, Tobias Schmidt, Florian Kiefer, Tiziano Gallo Cassarino, Martino Bertoni, Lorenza Bordoli, et al. Swiss-model: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic acids research, 42(W1):W252–W258, 2014.

[11] Kristian W Kaufmann, Gordon H Lemmon, Samuel L DeLuca, Jonathan H Sheehan, and Jens Meiler. Practically useful: what the rosetta protein modeling suite can do for you. Biochemistry, 49(14):2987–2998, 2010.

[12] Dong Xu and Yang Zhang. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins: Structure, Function, and Bioinformatics, 80(7):1715–1735, 2012.

[13] R Evans, J Jumper, J Kirkpatrick, L Sifre, TFG Green, C Qin, A Zidek, A Nelson, A Bridgland, H Penedones, et al. De novo structure prediction with deeplearning based scoring. Annu Rev Biochem, 77:363–382, 2018.

[14] Joerg Schaarschmidt, Bohdan Monastyrskyy, Andriy Kryshtafovych, and Alexandre MJJ Bonvin. Assessment of contact predictions in casp12: Coevolution and deep learning coming of age. Proteins: Structure, Function, and Bioinformatics, 86:51–66, 2018.

[15] David Baker and Andrej Sali. Protein structure prediction and structural genomics. Science, 294(5540):93–96, 2001.

[16] Gang Xu, Tianqi Ma, Tianwu Zang, Weitao Sun, Qinghua Wang, and Jianpeng Ma. Opus-dosp: A distance-and orientation-dependent all-atom potential derived from side-chain packing. Journal of molecular biology, 429(20):3113–3120, 2017.

[17] Robert B Best, Jeetain Mittal, Michael Feig, and Alexander D MacKerell Jr. Inclusion of many-body effects in the additive charmm protein cmap potential results in enhanced cooperativity of α-helix and β-hairpin formation. Biophysical journal, 103(5):1045–1051, 2012.

[18] Devleena Shivakumar, Joshua Williams, Yujie Wu, Wolfgang Damm, John Shelley, and Woody Sherman. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the opls force field. Journal of chemical theory and computation, 6(5):1509–1519, 2010.

[19] Yuedong Yang and Yaoqi Zhou. Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins: Structure, Function, and Bioinformatics, 72(2):793–803, 2008.

[20] Hongyi Zhou and Jeffrey Skolnick. Goap: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophysical journal, 101(8):2043–2052, 2011.

[21] Chao Zhang, George Vasmatzis, James L Cornette, and Charles DeLisi. Determination of atomic desolvation energies from the structures of crystallized proteins. Journal of molecular biology, 267(3):707–726, 1997.

[22] Hongyi Zhou and Yaoqi Zhou. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein science, 11(11):2714–2726, 2002.

[23] Mingyang Lu, Athanasios D Dousis, and Jianpeng Ma. Opus-psp: an orientation-dependent statistical all-atom potential derived from side-chain packing. Journal of molecular biology, 376(1):288–301, 2008.

[24] Alan M Poole and Rama Ranganathan. Knowledge-based potentials in protein design. Current opinion in structural biology, 16(4):508–513, 2006.

[25] Jian Zhang and Yang Zhang. A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PloS one, 5(10):e15386, 2010.

[26] Carol A Rohl, Charlie EM Strauss, Kira MS Misura, and David Baker. Protein structure prediction using rosetta. In Methods in enzymology, volume 383, pages 66–93. Elsevier, 2004.

[27] Juyong Lee, Jinhyuk Lee, Takeshi N Sasaki, Masaki Sasai, Chaok Seok, and Jooyoung Lee. De novo protein structure prediction by dynamic fragment assembly and conformational space annealing. Proteins: Structure, Function, and Bioinformatics, 79(8):2403–2417, 2011.

[28] Jooyoung Lee, Peter L Freddolino, and Yang Zhang. Ab initio protein structure prediction. In From protein structure to function with bioinformatics, pages 3– 35. Springer, 2017.

[29] Mario Garza-Fabre, Shaun M Kandathil, Julia Handl, Joshua Knowles, and Simon C Lovell. Generating, maintaining, and exploiting diversity in a memetic algorithm for protein structure prediction. Evolutionary computation, 24(4):577–607, 2016.

[30] Leonardo Correa, Bruno Borguesan, Camilo Farf´an, Mario Inostroza-Ponta, and M´arcio Dorn. A memetic algorithm for 3d protein structure prediction problem. IEEE/ACM transactions on computational biology and bioinformatics, 15(3):690–704, 2018.

[31] Seung Hwan Hong, InSuk Joung, Jose C Flores-Canales, Balachandran Manavalan, Qianyi Cheng, Seungryong Heo, Jong Yun Kim, Sun Young Lee, Mikyung Nam, Keehyoung Joo, et al. Protein structure modeling and refine- ment by global optimization in casp12. Proteins: Structure, Function, and Bioinformatics, 86:122–135, 2018.

[32] Shangce Gao, Catherine Vairappan, Yan Wang, Qiping Cao, and Zheng Tang. Gravitational search algorithm combined with chaos for unconstrained numerical optimization. Applied Mathematics and Computation, 231:48–62, 2014.

[33] Shangce Gao, Yirui Wang, Jiahai Wang, and JiuJun Cheng. Understanding differential evolution: A poisson law derived from population interaction network. Journal of computational science, 21:140–149, 2017.

[34] Junkai Ji, Shuangbao Song, Cheng Tang, Shangce Gao, Zheng Tang, and Yuki Todo. An artificial bee colony algorithm search guided by scale-free networks. Information Sciences, 473:142–165, 2019.

[35] Shangce Gao, Yang Yu, Yirui Wang, Jiahai Wang, Jiujun Cheng, and MengChu Zhou. Chaotic local search-based differential evolution algorithms for optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019.

[36] Shangce Gao, Yirui Wang, Jiujun Cheng, Yasuhiro Inazumi, and Zheng Tang. Ant colony optimization with clustering for solving the dynamic location routing problem. Applied Mathematics and Computation, 285:149–173, 2016.

[37] Zahid Halim and Tufail Muhammad. Quantifying and optimizing visualization: An evolutionary computing-based approach. Information Sciences, 385:284– 313, 2017.

[38] Zhenyu Song, Yajiao Tang, Xingqian Chen, Shuangbao Song, Shuangyu Song, and Shangce Gao. A preference-based multi-objective evolutionary strategy for ab initio prediction of proteins. In 2017 International Conference on Progress in Informatics and Computing (PIC), pages 7–12. IEEE, 2017.

[39] Shangce Gao, Mengchu Zhou, Yirui Wang, Jiujun Cheng, Hanaki Yachi, and Jiahai Wang. Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE transactions on neural networks and learning systems, 30(2):601–614, 2018.

[40] Junkai Ji, Shuangbao Song, Yajiao Tang, Shangce Gao, Zheng Tang, and Yuki Todo. Approximate logic neuron model trained by states of matter search algorithm. Knowledge-Based Systems, 163:120–130, 2019.

[41] Shuangyu Song, Xingqian Chen, Cheng Tang, Shuangbao Song, Zheng Tang, and Yuki Todo. Training an approximate logic dendritic neuron model using social learning particle swarm optimization algorithm. IEEE Access, 7:141947– 141959, 2019.

[42] Frederico T Silva, Mateus X Silva, and Jadson C Belchior. A new genetic algorithm approach applied to atomic and molecular cluster studies. Frontiers in chemistry, 7, 2019.

[43] Rainer Storn and Kenneth Price. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4):341–359, 1997.

[44] Swagatam Das and Ponnuthurai Nagaratnam Suganthan. Differential evolution: A survey of the state-of-the-art. IEEE transactions on evolutionary computation, 15(1):4–31, 2011.

[45] Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai N Suganthan. Recent advances in differential evolution–an updated survey. Swarm and Evolutionary Computation, 27:1–30, 2016.

[46] Rohan Mukherjee, Gyana Ranjan Patra, Rupam Kundu, and Swagatam Das. Cluster-based differential evolution with crowding archive for niching in dynamic environments. Information Sciences, 267:58–82, 2014.

[47] Xiao-gen Zhou, Gui-jun Zhang, Xiao-hu Hao, and Li Yu. A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization. Computers & Operations Research, 75:132–149, 2016.

[48] Mostafa Z Ali, Noor H Awad, Ponnuthurai Nagaratnam Suganthan, and Robert G Reynolds. An adaptive multipopulation differential evolution with dynamic population reduction. IEEE transactions on cybernetics, 47(9):2768– 2779, 2016.

[49] Xiao-Gen Zhou and Gui-Jun Zhang. Differential evolution with underestimation-based multimutation strategy. IEEE transactions on cybernetics, 49(4):1353–1364, 2018.

[50] Pinar Civicioglu and Erkan Besdok. Bernstain-search differential evolution algorithm for numerical function optimization. Expert Systems with Applications, 138:112831, 2019.

[51] Yong Zhang, Dun-wei Gong, Xiao-zhi Gao, Tian Tian, and Xiao-yan Sun. Binary differential evolution with self-learning for multi-objective feature selection. Information Sciences, 507:67–85, 2020.

[52] Seyed Mohammad Seyedpoor and Mohammad Hossein Nopour. A two-step method for damage identification in moment frame connections using support vector machine and differential evolution algorithm. Applied Soft Computing, 88:106008, 2020.

[53] Christiane Regina Soares Brasil, Alexandre Claudio Botazzo Delbem, and Fernando Lu´ıs Barroso da Silva. Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction. Journal of computational chemistry, 34(20):1719–1734, 2013.

[54] Sandra M Venske, Richard A Gon¸calves, Elaine M Benelli, and Myriam R Delgado. Ademo/d: an adaptive differential evolution for protein structure prediction problem. Expert Systems with Applications, 56:209–226, 2016.

[55] Greg´orio K Rocha, Karina B Dos Santos, Jaqueline S Angelo, Fabio L Custodio, Helio JC Barbosa, and Laurent E Dardenne. Inserting co-evolution information from contact maps into a multiobjective genetic algorithm for protein structure prediction. In 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1–8. IEEE, 2018.

[56] Shangce Gao, Shuangbao Song, Jiujun Cheng, Yuki Todo, and Mengchu Zhou. Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction. IEEE/ACM transactions on computational biology and bioinformatics, 15(4):1365–1378, 2018.

[57] Shuangbao Song, Shangce Gao, Xingqian Chen, Dongbao Jia, Xiaoxiao Qian, and Yuki Todo. Aimoes: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction. Knowledge-Based Systems, 146:58–72, 2018.

[58] Shuangbao Song, Junkai Ji, Xingqian Chen, Shangce Gao, Zheng Tang, and Yuki Todo. Adoption of an improved pso to explore a compound multiobjective energy function in protein structure prediction. Applied Soft Computing, 72:539–551, 2018.

[59] Ahmed Bin Zaman and Amarda Shehu. Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction. BMC bioinformatics, 20(1):211, 2019.

[60] Vincenzo Cutello, Giuseppe Narzisi, and Giuseppe Nicosia. A multi-objective evolutionary approach to the protein structure prediction problem. Journal of The Royal Society Interface, 3(6):139–151, 2006.

[61] Bruno Borguesan, Mariel Barbachan e Silva, Bruno Grisci, Mario InostrozaPonta, and M´arcio Dorn. Apl: An angle probability list to improve knowledgebased metaheuristics for the three-dimensional protein structure prediction. Computational biology and chemistry, 59:142–157, 2015.

[62] Zhang Guijun, Ma Laifa, Wang Xiaoqi, and Zhou Xiaogen. Secondary structure and contact guided differential evolution for protein structure prediction IEEE/ACM Transactions on Computational Biology and Bioinformatics, pages 1–1, 2018.

[63] Vincenzo Cutello, Giuseppe Narzisi, and Giuseppe Nicosia. Computational studies of peptide and protein structure prediction problems via multiobjective evolutionary algorithms. In Multiobjective problem solving from nature, pages 93–114. Springer, 2008.

[64] Daniel WA Buchan, Federico Minneci, Tim CO Nugent, Kevin Bryson, and David T Jones. Scalable web services for the psipred protein analysis workbench. Nucleic acids research, 41(W1):W349–W357, 2013.

[65] Cyrus Ahmadi Toussi and Javad Haddadnia. Improving protein secondary structure prediction: the evolutionary optimized classification algorithms. Structural Chemistry, 30(4):1257–1266, Aug 2019.

[66] Grzegorz Rozenberg, Thomas B¨ack, and Joost N Kok. Handbook of natural computing. Springer, 2012.

[67] Javier [Del Ser], Eneko Osaba, Daniel Molina, Xin-She Yang, Sancho SalcedoSanz, David Camacho, Swagatam Das, Ponnuthurai N. Suganthan, Carlos A. [Coello Coello], and Francisco Herrera. Bio-inspired computation: Where we stand and what’s next. Swarm and Evolutionary Computation, 48:220 – 250, 2019.

[68] Rudolf Kruse, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. Computational intelligence: a methodological introduction. Springer, 2016.

[69] Tianle Zhou, Chaoyi Chu, Shuangbao Song, Yirui Wang, and Shangce Gao. A dendritic neuron model for exchange rate prediction. In 2015 IEEE International Conference on Progress in Informatics and Computing (PIC), pages 10–14, Dec 2015.

[70] Ying Yu, Shuangbao Song, Tianle Zhou, Hanaki Yachi, and Shangce Gao. Forecasting house price index of china using dendritic neuron model. In 2016 International Conference on Progress in Informatics and Computing (PIC), pages 37–41, 2016.

[71] Junkai Ji, Shangce Gao, Jiujun Cheng, Zheng Tang, and Yuki Todo. An approximate logic neuron model with a dendritic structure. Neurocomputing, 173:1775 – 1783, 2016.

[72] Seyed Mohammad Mirjalili, Jin Song Dong, Ali Safa Sadiq, and Hossam Faris. Genetic algorithm: Theory, literature review, and application in image reconstruction. In Nature-Inspired Optimizers, 2019.

[73] Zhida Deng, Mihai D. Rotaru, and Jan K. Sykulski. Kriging assisted surrogate evolutionary computation to solve optimal power flow problems. IEEE Transactions on Power Systems, 35(2):831–839, 2020.

[74] Farid Ghareh Mohammadi, M. Hadi Amini, and Hamid R. Arabnia. Evolutionary Computation, Optimization, and Learning Algorithms for Data Science, pages 37–65. Springer International Publishing, Cham, 2020.

[75] Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M Fonseca, and Viviane Grunert Da Fonseca. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on evolutionary computation, 7(2):117–132, 2003.

[76] Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on evolutionary computation, 11(6):712–731, 2007.

[77] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182–197, 2002.

[78] Carlos A Coello Coello, Gregorio Toscano Pulido, and M Salazar Lechuga. Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3):256–279, 2004.

[79] Yousef Abdi and Mohammad-Reza Feizi-Derakhshi. Hybrid multi-objective evolutionary algorithm based on search manager framework for big data optimization problems. Applied Soft Computing, 87:105991, 2020.

[80] Ruochen Liu, Runan Zhou, Rui Ren, Jiangdi Liu, and Licheng Jiao. Multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition. Information Sciences, 509:420–436, 2020.

[81] Fei Zou, Gary G Yen, and Lixin Tang. A knee-guided prediction approach for dynamic multi-objective optimization. Information Sciences, 509:193–209, 2020.

[82] Dunwei Gong, Yiping Liu, and Gary G Yen. A meta-objective approach for many-objective evolutionary optimization. Evolutionary computation, 28(1):1– 25, 2020.

[83] Seyedali Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili, and Leandro dos S Coelho. Multi-objective grey wolf optimizer: a novel algorithm for multicriterion optimization. Expert Systems with Applications, 47:106–119, 2016.

[84] Anupam Trivedi, Dipti Srinivasan, Krishnendu Sanyal, and Abhiroop Ghosh. A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Transactions on Evolutionary Computation, 21(3):440–462, 2016.

[85] Ye Tian, Ran Cheng, Xingyi Zhang, Fan Cheng, and Yaochu Jin. An indicatorbased multiobjective evolutionary algorithm with reference point adaptation for better versatility. IEEE Transactions on Evolutionary Computation, 22(4):609– 622, 2017.

[86] Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. Multiobjective evolutionary algorithms: A sur- vey of the state of the art. Swarm and Evolutionary Computation, 1(1):32–49, 2011.

[87] Min-yi Shen and Andrej Sali. Statistical potential for assessment and prediction of protein structures. Protein science, 15(11):2507–2524, 2006.

[88] Yuedong Yang and Yaoqi Zhou. Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related allatom statistical energy functions. Protein science, 17(7):1212–1219, 2008.

[89] Yang Zhang, Haijun Zhou, and Zhong-Can Ou-Yang. Stretching single-stranded dna: interplay of electrostatic, base-pairing, and base-pair stacking interactions. Biophysical journal, 81(2):1133–1143, 2001.

[90] Matthew J O’Meara, Andrew Leaver-Fay, Michael D Tyka, Amelie Stein, Kevin Houlihan, Frank DiMaio, Philip Bradley, Tanja Kortemme, David Baker, Jack Snoeyink, et al. Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with rosetta. Journal of chemical theory and computation, 11(2):609–622, 2015.

[91] Xiaogen Zhou, Jun Hu, Chengxin Zhang, Guijun Zhang, and Yang Zhang. Assembling multidomain protein structures through analogous global structural alignments. Proceedings of the National Academy of Sciences, 116(32):15930– 15938, 2019.

[92] Jingqiao Zhang and Arthur C Sanderson. Jade: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5):945–958, 2009.

[93] A Kai Qin, Vicky Ling Huang, and Ponnuthurai N Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE transactions on Evolutionary Computation, 13(2):398–417, 2009.

[94] Glennie Helles. A comparative study of the reported performance of ab initio protein structure prediction algorithms. Journal of the royal society interface, 5(21):387–396, 2007.

[95] Pierrick Craveur, Agnel Praveen Joseph, Pierre Poulain, Alexandre G de Brevern, and Joseph Rebehmed. Cis–trans isomerization of omega dihedrals in proteins. Amino acids, 45(2):279–289, 2013.

[96] Roland L Dunbrack Jr. Rotamer libraries in the 21st century. Current opinion in structural biology, 12(4):431–440, 2002.

[97] Andriy Kryshtafovych, Bohdan Monastyrskyy, Krzysztof Fidelis, Torsten Schwede, and Anna Tramontano. Assessment of model accuracy estimations in casp12. Proteins: Structure, Function, and Bioinformatics, 86:345–360, 2018.

[98] Zahid Halim et al. Optimizing the minimum spanning tree-based extracted clusters using evolution strategy. Cluster Computing, 21(1):377–391, 2018.

[99] Yang Zhang and Jeffrey Skolnick. Spicker: a clustering approach to identify near-native protein folds. Journal of computational chemistry, 25(6):865–871, 2004.

[100] Jingfen Zhang and Dong Xu. Fast algorithm for population-based protein structural model analysis. Proteomics, 13(2):221–229, 2013.

[101] C. A. Coello Coello. Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1):28–36, Feb 2006.

[102] Yang Zhang and Jeffrey Skolnick. Scoring function for automated assessment of protein structure template quality. Proteins: Structure, Function, and Bioinformatics, 57(4):702–710, 2004.

[103] Adam Zemla. Lga: a method for finding 3d similarities in protein structures. Nucleic acids research, 31(13):3370–3374, 2003.

[104] Kevin Molloy, Sameh Saleh, and Amarda Shehu. Probabilistic search and energy guidance for biased decoy sampling in ab initio protein structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 10(5):1162–1175, 2013.

[105] Leonardo de Lima Corrˆea, Bruno Borguesan, Mathias J Krause, and M´arcio Dorn. Three-dimensional protein structure prediction based on memetic algorithms. Computers & Operations Research, 91:160–177, 2018.

[106] Oliviero Carugo and S´andor Pongor. A normalized root-mean-spuare distance for comparing protein three-dimensional structures. Protein science, 10(7):1470–1473, 2001.

[107] Luciano A Abriata, Giorgio E Tam`o, Bohdan Monastyrskyy, Andriy Kryshtafovych, and Matteo Dal Peraro. Assessment of hard target modeling in casp12 reveals an emerging role of alignment-based contact prediction methods. Proteins: Structure, Function, and Bioinformatics, 86:97–112, 2018.

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