Abstract
Protein folding optimization (PFO) is an NP-hard problem. The Butterfly Optimization Algorithm (BOA) is a recently invented meta-heuristic algorithm that has outperformed current algorithms on a variety of issues. Figure out the structure of a protein is a hard task. Many biomedical operations depend on it. Its already tried by many types of calculation but no one gets the appropriate result. For protein figure analysis but steel have a plethora of place to do some task. That’s why we are going to use the BOA algorithm. And we get enough good results regarding this problem. The three operators of the BOA are (1) Initialization phase, (2) Iteration phase, and (3) Final phase. We have also created a mechanism to obtain the proper structure which is a repair mechanism. Test results show that it works well when the BOA performs in the PFO problem which is better than many other calculations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, C., Hou, C., Zhang, Q., Wei, X.: Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model. J. Molec. Model. 19(9), 3883–3891 (2013)
Mansour, N., Kanj, F., Khachfe, H.: Particle swarm optimization approach for protein structure prediction in the 3D HP model. Interdisc. Sci. Comput. Life Sci. 4(3), 190–200 (2012)
Lin, C.-J., Su, S.-C.: Protein 3D HP model folding simulation using a hybrid of genetic algorithm and particle swarm optimization. Int. J. Fuzzy Syst. 13(2), 1–8 (2011)
Garza-Fabre, M., Rodriguez-Tello, E., Toscano-Pulido, G.: Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction. Comput. Oper. Res. 53, 128–153 (2015)
Islam, M.K., Chetty, M.: Clustered memetic algorithm with local heuristics for ab initio protein structure prediction. IEEE Trans. Evol. Comput. 17(4), 558–576 (2012)
Zhang, X., Cheng, W.: Protein 3D structure prediction by improved tabu search in off-lattice AB model. In: 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 184–187 (2008)
Custódio, F.L., Barbosa, H.J.C., Dardenne, L.E.: Investigation of the three-dimensional lattice HP protein folding model using a genetic algorithm. Genet. Molec. Biol. 27(4), 611–615 (2004). https://doi.org/10.1590/S1415-47572004000400023
Palu, D., Alessandro, A.D., Pontelli, E.: Heuristics, optimizations, and parallelism for protein structure prediction in CLP (FD). In: Proceedings of the 7th ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming, pp. 230–241 (2005)
Li, T., Zhou, C., Wang, B., Xiao, B., Zheng, X.: A hybrid algorithm based on artificial bee colony and pigeon inspired optimization for 3D protein structure prediction. J. Bionanosci. 12(1), 100–108 (2018)
Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing 23(3), 715–734 (2018). https://doi.org/10.1007/s00500-018-3102-4
Bošković, B., Brest, J.: Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobic-polar model and cubic lattice. Appl. Soft Comput. 45, 61–70 (2016)
Cutello, V., Nicosia, G., Pavone, M., Timmis, J.: An immune algorithm for protein structure prediction on lattice models. IEEE Trans. Evol. Comput. 11(1), 101–117 (2007)
Bazzoli, A., Tettamanzi, A.G.B.: A memetic algorithm for protein structure prediction in a 3D-lattice HP model. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 1–10. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24653-4_1
Custódio, F.L., Barbosa, H.J.C., Dardenne, L.E.: A multiple minima genetic algorithm for protein structure prediction. Appl. Soft Comput. 15, 88–99 (2014). https://doi.org/10.1016/j.asoc.2013.10.029
Chatterjee, S., Smrity, R.A., Islam, M.R.: Protein structure prediction using chemical reaction optimization. In: 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 321–326. IEEE (2016)
Angela, U., Sylvester, Adetayo: Protein secondary structure prediction using deep neural network and particle swarm optimization algorithm. Int. J. Comput. Appl. 181(28), 1–8 (2018). https://doi.org/10.5120/ijca2018918070
Islam, M.R., Smrity, R.A., Chatterjee, S., Mahmud, M.R.: Optimization of protein folding using chemical reaction optimization in HP cubic lattice model. Neural Comput. Appl. 32(8), 3117–3134 (2019). https://doi.org/10.1007/s00521-019-04447-8
Chatterjee, S., Shill, P.C.: Protein folding optimization in a hydrophobic-polar model for predicting tertiary structure using fruit fly optimization algorithm. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Karim, M.S., Chatterjee, S., Hira, A., Islam, T., Islam, R. (2023). Protein Folding Optimization Using Butterfly Optimization Algorithm. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_61
Download citation
DOI: https://doi.org/10.1007/978-3-031-34622-4_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34621-7
Online ISBN: 978-3-031-34622-4
eBook Packages: Computer ScienceComputer Science (R0)