Ferdowsi University of Mashhad

An Evolutionary Algorithm to Predict Super Secondary Structures of Proteins from Secondary Ones, A Case Study: β-LACTAMAZE Enzyme

Document Type : Research Articles

Authors

1 Department of Computer, University of Kashan, Kashan, Iran

2 Department of Biology, Tarbiat Modares University, Tehran, Iran

Abstract
The protein’s motifs (called super secondary structures) are dense three-dimensional structures of proteins consisting of several secondary structures in a specific geometric arrangement. The prediction of motifs is a matter of concern and has been studied. The previous studies dealt with motif prediction based on the polypeptide chain; however, the prediction of motifs based on the secondary structures leads to more accurate prediction. This study aims to address such a prediction. First, several secondary structures are constructed and then, based on the energy level and using a metaheuristic (evolutionary) algorithm called Imperialist Competitive Algorithm. (ICA) The protein’s motifs are predicted. The advantage of our approach over existing approaches is that secondary structural data as input to our algorithm leads to a more accurate prediction that is closer to the real protein third than previous algorithms. We applied our method to predict super secondaries of the enzyme βLACTAMASE, whose specification was obtained from the PDB file in Yasara. This enzyme is produced by bacteria and provides multi-resistance to antibiotics βLACTAMA. Then we evaluated our prediction using Root-Mean-Square Deviation (RMSD). It shows the average distance between the two proteins structurally having the same alignment. Having determined the structural alignment of the two proteins, we determined the similarity of their 3D structures using RMSD. If the RMSD between two structures is less than 2, it denotes they are very similar. Accordingly, we used RMSD to show how much similarity exists between the motif obtained by our proposed algorithm for βLACTAMASE and its native structure.

Keywords


Arab, S., Sadeghi, M., Eslahchi, C., Pezeshk, H. and Sheari, A. (2010) A pairwise residue contact area-based mean force potential for discrimination of native protein structure, BMC Bioinformatics, 11:1, 16.
Bouziane, H., Messabih, B. and Chouarfia, A. (2015) Effect of simple ensemble methods on protein secondary structure prediction, Soft Computing, 19:6, 1663-1678.
Bu, Y. and Zhu, Y. (2009) Artificial immune ant colony algorithm and its application, in 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. IEEE, 75-80.
Burkowski, F.J. (2008) Structural bioinformatics: an algorithmic approach. Chapman and Hall/CRC.
Coutsias, E.A. and Wester, M.J. (2019) RMSD and symmetry, Journal of Computational Chemistry, 40:15, 1496-1508.
Elahi, A. and Babamir, S.M. (2018) Identification of essential proteins based on a new combination of topological and biological features in weighted protein-protein interaction networks, IET Systems Biology, 12:6, 247-257.
Enireddy, V., Karthikeyan, C. and Babu, D.V. (2022) Onehotencoding and LSTM-based deep learning models for protein secondary structure prediction, Soft Computing, 26:8, 3825-3836.
Gao, M. and Skolnick, J. (2021) A general framework to learn tertiary structure for protein sequence characterization, Frontiers in Bioinformatics, 1, 1-12.
Guyeux, C., Cote, N.M.-L., Bahi, J.M. and Bienia, W. (2014) Is protein folding problem really a NP-complete one? First investigations, Journal of Bioinformatics and Computational Biology, 12:01, 1350017.
Huang, B. et al. (2023) Protein structure prediction: challenges, advances, and the shift of research paradigms, Genomics, Proteomics & Bioinformatics, 21:5, 913-925.
Kaveh, A. and Bakhshpoori, T. (2019) Chapter 6: imperialist competitive algorithm, in Metaheuristics: outlines, MATLAB codes and examples. Springer.
Khaji, E., Karami, M. and Garkani-Nejad, Z. (2016) 3D protein structure prediction using imperialist competitive algorithm and half sphere exposure prediction, Journal of Theoretical Biology, 391, 81-87.
Kuhlman, B. and Bradley, P. (2019) Advances in protein structure prediction and design, Nature Reviews Molecular Cell Biology, 20:11, 681-697.
Li, Y., Zhou, C. and Zheng, X. (2015) Artificial bee colony algorithm for the protein structure prediction based on the toy model, Fundamenta Informaticae, 136:3, 241-252.
Lin, J., Zhong, Y., Li, E., Lin, X. and Zhang, H. (2018) Multi-agent simulated annealing algorithm with parallel adaptive multiple sampling for protein structure prediction in AB off-lattice model, Applied Soft Computing, 62, 491-503.
Lin, X., Zhang, X. and Zhou, F. (2014) Protein structure prediction with local adjust tabu search algorithm, BMC Bioinformatics, 15:S15, S1.
MacCarthy, E., Perry, D. and Kc, D.B. (2019) Advances in protein super-secondary structure prediction and application to protein structure prediction, in Protein supersecondary structures: methods and protocols. Springer, 15-45.
Marquez-Chamorro, A.E. et al. (2015) Soft computing methods for the prediction of protein tertiary structures: a survey, Applied Soft Computing, 35, 398-410.
Muñoz, V. (ed.) (2022) Protein folding: methods and protocols. New York: Springer.
Nabil, B. and Sadek, B. (2020) Protein structure prediction in the HP model using scatter search algorithm, in The 4th International Symposium on Informatics and its Applications (ISIA). IEEE, 1-5.
Rashid, M. et al. (2016) An enhanced genetic algorithm for ab initio protein structure prediction, IEEE Transactions on Evolutionary Computation, 20:4, 627-644.
Rashid, M. et al. (2019) Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse monte carlo sampling, BMC Bioinformatics, 20:S3, 118.
Rayesha, S.M.S., Banu, W.A. and Priya, S. (2023) The prediction of protein structure using neural network, in International Conference on Data Management, Analytics and Innovation. Springer, 1021-1028.
Saudagar, P. and Tripathi, T. (eds.) (2023) Protein folding dynamics and stability: experimental and computational methods. Springer.
Sekhar, S.R.M., Matt, S.D. and Mahadevachar, V.K. (2023) Protein tertiary structure prediction by integrating ant colony optimization with path relinking and structure knowledge, International Journal of Information Technology, 15:3, 1399-1405.
Tantar, A.-A., Melab, N. and Talbi, E.-G. (2008) A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction, Soft Computing, 12:12, 1185-1198.
Varela, D. and Santos, J. (2022) Protein structure prediction in an atomic model with differential evolution integrated with the crowding niching method, Natural Computing, 21:4, 537-551.
Wang, F., Xu, C., Jiang, S. and Xu, F. (2020) Application of improved intelligent ant colony algorithm in protein folding prediction, Journal of Algorithms & Computational Technology, 14, 1-7.
Webb, B. and Sali, A. (2014) Comparative protein structure modeling using MODELLER, Current Protocols in Bioinformatics, 47:1, 5.6.1-5.6.32.
Yang, L., Qing, Y., Liwei, W. and Jian, P. (2018) Learning structural motif representations for efficient protein structure search, Bioinformatics, 34:17, i773-i780.
Yousef, M., Abdelkader, T. and ElBahnasy, K. (2017) A hybrid model to predict proteins tertiary structure, in 2017 12th International Conference on Computer Engineering and Systems (ICCES). IEEE, 85-91.
Yu, S., Li, X., Tian, X. and Pang, M. (2022) Protein structure prediction based on particle swarm optimization and tabu search strategy, BMC Bioinformatics, 23, 537.
Send comment about this article
Enter Name.
Enter a valid email address.
Enter a vaid affiliation.
Enter comments (At leaset 10 words)
CAPTCHA Image
Enter Security Code Correctly.

  • Receive Date 17 April 2024
  • Revise Date 01 December 2024
  • Accept Date 01 December 2024