research-article

An improved artificial bee colony algorithm for 3D protein structure prediction

Authors:

Mandong HuAuthors Info & Claims

ICBEA '17: Proceedings of the 2017 International Conference on Biometrics Engineering and Application

Pages 7 - 12

https://doi.org/10.1145/3077829.3077831

Published: 21 April 2017 Publication History

Abstract

One of the key problems in the field of bioinformatics is protein structure prediction. The thermodynamic hypothesis demonstrates that protein's energy is the lowest in nature state. So protein's structure can be gotten directly by protein sequence's free-energy. In this paper, an improved algorithm based on three-dimensional AB off-lattice model to improve local search and global optimization ability of artificial bee colony algorithm has been presented. The simulation experiment shows that it can effectively search the lowest free-energy in the condition of keeping high accuracy. The experimental results indicate that the minimum energy from the improved artificial bee colony algorithm is better than other similar algorithms, and with the increase of protein sequence's length, this algorithm has better performance.

References

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Li, B., Li, Y., and Gong, L.G. 2014. Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model. Eng. Appl Artif. Intel. 27, 1(Jan. 2014), 70--79.

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Cited By

Hosny MHayel RAltwaijry N(2023)Composing Multiple Online Exams: The Bees Algorithm SolutionApplied Sciences10.3390/app13231271013:23(12710)Online publication date: 27-Nov-2023
https://doi.org/10.3390/app132312710
Hattori LBenitez CGutoski MAquino NLopes H(2018)A Novel Approach to Protein Folding Prediction based on Long Short-Term Memory Networks: A Preliminary Investigation and Analysis2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489514(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/IJCNN.2018.8489514

Index Terms

An improved artificial bee colony algorithm for 3D protein structure prediction
1. Computing methodologies
  1. Modeling and simulation

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Information

Published In

cover image ACM Other conferences

ICBEA '17: Proceedings of the 2017 International Conference on Biometrics Engineering and Application

April 2017

61 pages

ISBN:9781450348713

DOI:10.1145/3077829

Conference Chairs:
David Zhang
Hong Kong Polytechnic University, Hong Kong
,
Gautam Sethi
National University of Singapore, Clinical Research Centre, Singapore
,
Program Chairs:
Raymond Veldhuis
University of Twente, The Netherlands
,
Yasushi Yagi
Osaka University, Japan
,
Andrew Teoh
Yonsei University, South Korea

Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2017

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ICBEA '17

ICBEA '17: 2017 International Conference on Biometrics Engineering and Application

April 21 - 23, 2017

Hong Kong, Hong Kong

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Cited By

Hosny MHayel RAltwaijry N(2023)Composing Multiple Online Exams: The Bees Algorithm SolutionApplied Sciences10.3390/app13231271013:23(12710)Online publication date: 27-Nov-2023
https://doi.org/10.3390/app132312710
Hattori LBenitez CGutoski MAquino NLopes H(2018)A Novel Approach to Protein Folding Prediction based on Long Short-Term Memory Networks: A Preliminary Investigation and Analysis2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489514(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/IJCNN.2018.8489514

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