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An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction

BACKGROUND: Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein fro...

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Autores principales: Su, Shih-Chieh, Lin, Cheng-Jian, Ting, Chuan-Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289079/
https://www.ncbi.nlm.nih.gov/pubmed/22166054
http://dx.doi.org/10.1186/1477-5956-9-S1-S19
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author Su, Shih-Chieh
Lin, Cheng-Jian
Ting, Chuan-Kang
author_facet Su, Shih-Chieh
Lin, Cheng-Jian
Ting, Chuan-Kang
author_sort Su, Shih-Chieh
collection PubMed
description BACKGROUND: Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model can give a better structure modeling and prediction for proteins with short primary amino acid sequences. METHODS: This paper proposes a hybrid of hill-climbing and genetic algorithm (HHGA) based on elite-based reproduction strategy for protein structure prediction on the 2D triangular lattice. RESULTS: The simulation results show that the proposed HHGA can successfully deal with the protein structure prediction problems. Specifically, HHGA significantly outperforms conventional genetic algorithms and is comparable to the state-of-the-art method in terms of free energy. CONCLUSIONS: Thanks to the enhancement of local search on the global search, the proposed HHGA achieves promising results on the 2D triangular protein structure prediction problem. The satisfactory simulation results demonstrate the effectiveness of the proposed HHGA and the utility of the 2D triangular lattice model for protein structure prediction.
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spelling pubmed-32890792012-02-29 An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction Su, Shih-Chieh Lin, Cheng-Jian Ting, Chuan-Kang Proteome Sci Proceedings BACKGROUND: Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, DNA and RNA synthesis, and embryonic development. It has been a long-standing goal in molecular biology to predict the tertiary structure of a protein from its primary amino acid sequence. From visual comparison, it was found that a 2D triangular lattice model can give a better structure modeling and prediction for proteins with short primary amino acid sequences. METHODS: This paper proposes a hybrid of hill-climbing and genetic algorithm (HHGA) based on elite-based reproduction strategy for protein structure prediction on the 2D triangular lattice. RESULTS: The simulation results show that the proposed HHGA can successfully deal with the protein structure prediction problems. Specifically, HHGA significantly outperforms conventional genetic algorithms and is comparable to the state-of-the-art method in terms of free energy. CONCLUSIONS: Thanks to the enhancement of local search on the global search, the proposed HHGA achieves promising results on the 2D triangular protein structure prediction problem. The satisfactory simulation results demonstrate the effectiveness of the proposed HHGA and the utility of the 2D triangular lattice model for protein structure prediction. BioMed Central 2011-10-14 /pmc/articles/PMC3289079/ /pubmed/22166054 http://dx.doi.org/10.1186/1477-5956-9-S1-S19 Text en Copyright ©2011 Su et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Su, Shih-Chieh
Lin, Cheng-Jian
Ting, Chuan-Kang
An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction
title An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction
title_full An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction
title_fullStr An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction
title_full_unstemmed An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction
title_short An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction
title_sort effective hybrid of hill climbing and genetic algorithm for 2d triangular protein structure prediction
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3289079/
https://www.ncbi.nlm.nih.gov/pubmed/22166054
http://dx.doi.org/10.1186/1477-5956-9-S1-S19
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