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Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

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Abstract

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.

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References

  1. Mansour N, Kanj F, Khachfe H (2011) Enhanced genetic algorithm for protein structure prediction based on the HP model. Search Algorithms Appl 3:69

    Google Scholar 

  2. Soriano-Ursua MA, Correa-Basurto J, Trujillo-Ferrara JG et al (2011) Homology model and docking studies on porcine adrenoceptor: description of two binding sites. J Mol Model 17:2525–2538

    Article  CAS  Google Scholar 

  3. Bishop OT, Kroon M (2011) Study of protein complexes via homology modeling, applied to cysteine proteases and the protein inhibitors. J Mol Model 17:3163–3172

    Article  CAS  Google Scholar 

  4. Yang X, Huang C, He Z (2010) Protein folding simulations of 2D HP model by the genetic algorithm based on optimal secondary structures. Comput Biol Chem 34(137)

  5. Rother K, Rother M, Boniecki M et al (2011) RNA and protein 3D structure modeling: similarities and differences. J Mol Model 17:2325–2336

    Article  CAS  Google Scholar 

  6. Srivastava M, Gupta SK, Abhilash PC et al (2011) Structure prediction and binding sites analysis of curcin protein of Jatropha curcas using computational approachies. J Mol Model 18:2971–2979

    Article  Google Scholar 

  7. Chakravarty S, Ghersi D, Sanchez R (2011) Systematic asssessment of assuracy of comparative model of protein belonging to different structural fold classes. J Mol Model 17:2831–2837

    Article  CAS  Google Scholar 

  8. Curco D, Michaux C, Roussel G et al (2012) Stochastic simulation of structural properties of natively unfolded and denatured proteins. J Mol Model 18:4503–4516

    Google Scholar 

  9. Benitez CMV, Lopes HS (2009) A parallel genetic algorithm for protein folding prediction using 3D-HP side chain model. IEEE Congress on Evolutionary Computation, 1297, Trondheim, 18–21 May 2009

    Google Scholar 

  10. Subashini M, Devarajan PV (2011) Molecular dynamics simulation of drug uptake by polymer. J Mol Model 17:1141–1147

    Article  CAS  Google Scholar 

  11. Lin C-J, Shih-Chieh S (2011) Protein 3D HP model folding simulation using a hybrid of genetic algorithm and particle swarm optimization. Int J Fuzzy Syst 13:140–141

    Google Scholar 

  12. Zhang X, Cheng W (2008) Protein 3D structure prediction by improved Tabu Search in Off-Lattice AB model. IEEE 187

  13. Guo H, Lv Q, Wu J et al (2009) Solving 2D HP protein folding problem by parallel ant colonies. IEEE 2

  14. Mansour N, Kanj F, Khachfe H (2012) Particle swarm optimization approach for protein structure prediction in the 3D HP model. Intediscip Sci Comput Life Sci (4):190–280

  15. Wang M, Wang J (2011) A computerized protein-protein interaction modeling study of ampicillin antibody specificity in relation to biosensor development. J Mol Model 17:2873–2882

    Article  CAS  Google Scholar 

  16. Durham E, Dorr B, Woetzel N et al (2009) Solvent accessible surface area approximations for rapid and accurate protein structure prediciton. J Mol Model 15:1093–1108

    Article  CAS  Google Scholar 

  17. Li T, Wang J, Li Y et al (2011) Structure of the complex between Mucor pusillus pepsin and the key domain of k-casein for site-directed mutagenesis: a combined molecular modeling and docking approach. J Mol Model 17:1661–1668

    Article  CAS  Google Scholar 

  18. Mazzucco TN, Zanconato S, De Lucrezia D et al (2011) Design and dynamic simulation of minimal metallo-proteins. J Mol Model 17:2919–2925

    Article  CAS  Google Scholar 

  19. Zhou H, Lv Q (2009) A study on applying particle swarm optimization algorithm. Su Zhou College 15

  20. Chira C, Horvath D (2011) Evolutionary algorithm for protein structure prediction in lattice models. Analele Unibersitatii de Vest, vol 1, p 9

  21. Saraswathi S, Fernandez-Martinez JL, Kolinski A et al (2012) Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction. J Mol Model 18

  22. Yang Y, Liu H, Juan D et al (2011) A combined molecular modeling study on a series of pyrazole/isoxazole based human Hsp90 a inhibitors. J Mol Model 17:3241–3250

    Article  CAS  Google Scholar 

  23. Su S-C, Lin C-J, Ting C-K (2010) An effective hybrid of hill climbing and genetic algorithm for 2D triangular protein structure prediction. Proteome Sci 18:3

    Google Scholar 

  24. Chen X, Lv M, Zhao L et al (2011) An improved particle swarm optimization for protein folding prediction. IEEE 1:3–5

    Google Scholar 

  25. Glover F (1989) Tabu search. J Comput 1:190–206

    Google Scholar 

  26. Glover F (1990) Tabu search. J Comput 2:4–32

    Google Scholar 

  27. Zhang X, Wang T, Luo H et al (2010) 3D Protein structure prediction with genetic tabu search algorithm. BMC Syst Biol 4:3–4

    Article  CAS  Google Scholar 

  28. Wang X, Chen D (2006) The research of protein configuration forecasting based on mixed genetic algorithm. Wuhan University of Technology, pp 39–40

  29. Bui TN, Sundarra G (2005) An efficient genetic algorithm for predicting protein tertiary structures in the 2D HP model. Computer Science Program 385

  30. Zhang XL, Cheng W (2008) An improved tabu search algorithm for 3D protein folding problem, PRICAI 2008: Trends in Artificial Intelligence, vol 5351, 1104–1109

  31. Wang T, Zhang X (2010) 3D protein folding structure prediction with genetic tabu search algorithm. Wuhan University of Technology, BMC Systems Biology

  32. Kirbakaran P, Karthikeyan M, Singh KD et al (2012) In silico structural and functinal analysis of the human TOPK protein by structure modeling and molecular dynamics studies J Mol Model

  33. Chira C, Horvath D, Dumitrescu D (2011) Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction. BioData Mining 4

  34. Rego C, Li H, Glover F (2011) A filter-and-fan approach to the 2D lattice model of the protein folding problem. Ann Operations Res 188:389–414

    Google Scholar 

  35. Junyan Z, Jinming L (2011) Application of simulated annealing algorithm in prediction of protein structure. Master Thesis of Fujian Agriculture and Forestry University

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.31170797,30870573,61103057), the Program for Changjiang Scholars and Innovative Research Team in University (No.IRT1109), the Key Project of Chinese Ministry of Education (No.211036), the Program for Liaoning Excellent Talents in University (No.LR201003).

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Correspondence to Changjun Zhou.

Appendix

Appendix

Fig. 4
figure 4

Conformation of sequence of length 8

Fig. 5
figure 5

Conformation of sequence of length 34

Fig. 6
figure 6

Conformation of sequence of 1BXL

Fig. 7
figure 7

Conformation of sequence of 1EDP

Fig. 8
figure 8

Conformation of sequence of S1

Fig. 9
figure 9

Conformation of sequence of S5

Table 4 Results of comparison
Table 5 Sequences of length 27
Table 6 Results of comparison of length 27

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Zhou, C., Hou, C., Zhang, Q. et al. Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model. J Mol Model 19, 3883–3891 (2013). https://doi.org/10.1007/s00894-013-1907-8

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  • DOI: https://doi.org/10.1007/s00894-013-1907-8

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