Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm

Shuihua Wang; Lenan Wu; Yuankai Huo; Xueyan Wu; Hainan Wang; Yudong Zhang

doi:10.1007/978-3-319-46257-8_2

Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm

Shuihua Wang, Lenan Wu, Yuankai Huo, Xueyan Wu, Hainan Wang, Yudong Zhang^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

6 Citations (Scopus)

Abstract

In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components—(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings
Editors	Daoqiang Zhang, Yang Gao, Hujun Yin, Bin Li, Yun Li, Ming Yang, Frank Klawonn, Antonio J. Tallón-Ballesteros
Publisher	Springer Verlag
Pages	10-17
Number of pages	8
ISBN (Print)	9783319462561
DOIs	https://doi.org/10.1007/978-3-319-46257-8_2
Publication status	Published - 2016
Externally published	Yes
Event	17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 - Yangzhou, China Duration: 12 Oct 2016 → 14 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9937 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
Country/Territory	China
City	Yangzhou
Period	12/10/16 → 14/10/16

Keywords

Artificial immune system
Chaotic clonal genetic algorithm
Clonal selection algorithm
Hydrophobic-polar model
Protein folding

Access to Document

10.1007/978-3-319-46257-8_2

Cite this

Wang, S., Wu, L., Huo, Y., Wu, X., Wang, H., & Zhang, Y. (2016). Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm. In D. Zhang, Y. Gao, H. Yin, B. Li, Y. Li, M. Yang, F. Klawonn, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings (pp. 10-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9937 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46257-8_2

Wang, Shuihua ; Wu, Lenan ; Huo, Yuankai et al. / Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm. Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. editor / Daoqiang Zhang ; Yang Gao ; Hujun Yin ; Bin Li ; Yun Li ; Ming Yang ; Frank Klawonn ; Antonio J. Tallón-Ballesteros. Springer Verlag, 2016. pp. 10-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm",

abstract = "In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components—(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.",

keywords = "Artificial immune system, Chaotic clonal genetic algorithm, Clonal selection algorithm, Hydrophobic-polar model, Protein folding",

author = "Shuihua Wang and Lenan Wu and Yuankai Huo and Xueyan Wu and Hainan Wang and Yudong Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 ; Conference date: 12-10-2016 Through 14-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46257-8_2",

language = "English",

isbn = "9783319462561",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "10--17",

editor = "Daoqiang Zhang and Yang Gao and Hujun Yin and Bin Li and Yun Li and Ming Yang and Frank Klawonn and Tall{\'o}n-Ballesteros, {Antonio J.}",

booktitle = "Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings",

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Wang, S, Wu, L, Huo, Y, Wu, X, Wang, H & Zhang, Y 2016, Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm. in D Zhang, Y Gao, H Yin, B Li, Y Li, M Yang, F Klawonn & AJ Tallón-Ballesteros (eds), Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9937 LNCS, Springer Verlag, pp. 10-17, 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, Yangzhou, China, 12/10/16. https://doi.org/10.1007/978-3-319-46257-8_2

Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm. / Wang, Shuihua; Wu, Lenan; Huo, Yuankai et al.
Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. ed. / Daoqiang Zhang; Yang Gao; Hujun Yin; Bin Li; Yun Li; Ming Yang; Frank Klawonn; Antonio J. Tallón-Ballesteros. Springer Verlag, 2016. p. 10-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9937 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm

AU - Wang, Shuihua

AU - Wu, Lenan

AU - Huo, Yuankai

AU - Wu, Xueyan

AU - Wang, Hainan

AU - Zhang, Yudong

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components—(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.

AB - In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components—(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.

KW - Artificial immune system

KW - Chaotic clonal genetic algorithm

KW - Clonal selection algorithm

KW - Hydrophobic-polar model

KW - Protein folding

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U2 - 10.1007/978-3-319-46257-8_2

DO - 10.1007/978-3-319-46257-8_2

M3 - Conference Proceeding

AN - SCOPUS:84989869202

SN - 9783319462561

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 10

EP - 17

BT - Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings

A2 - Zhang, Daoqiang

A2 - Gao, Yang

A2 - Yin, Hujun

A2 - Li, Bin

A2 - Li, Yun

A2 - Yang, Ming

A2 - Klawonn, Frank

A2 - Tallón-Ballesteros, Antonio J.

PB - Springer Verlag

T2 - 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016

Y2 - 12 October 2016 through 14 October 2016

ER -

Wang S, Wu L, Huo Y, Wu X, Wang H, Zhang Y. Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm. In Zhang D, Gao Y, Yin H, Li B, Li Y, Yang M, Klawonn F, Tallón-Ballesteros AJ, editors, Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. Springer Verlag. 2016. p. 10-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46257-8_2

Predict two-dimensional protein folding based on hydrophobic-polar lattice model and chaotic clonal genetic algorithm

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