Skip to main content
SpringerLink
Log in

A novel approach for protein structure prediction based on an estimation of distribution algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Protein structure prediction is one of the major challenges in structural biology and has wide potential applications in biotechnology. However, the problem is faced with a difficult optimization requirement with particularly complex energy landscapes. The current article aims to present a novel approach namely AHEDA as an evolutionary-based solution to overcome the problem. AHEDA uses the hydrophobic-polar model to develop a robust and efficient evolutionary-based algorithm for protein structure prediction. The method utilizes an integrated estimation of distribution algorithm that attempts to optimize the search process and prevent the destruction of structural blocks. It also uses a stochastic local search to improve its accuracy. Based on a comprehensive comparison with other existing methods on 24 widely used benchmarks, AHEDA was shown to generate highly accurate predictions compared to the other similar methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(Reproduced with the permission from Garza-Fabre et al. 2015)

Fig. 2
Fig. 3

(Reproduced with the permission from Custódio et al. 2014)

Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Anfinsen C (1973) Principles that govern the folding of protein chains. Science 181(96):223–230

    Article  Google Scholar 

  • Babaei S, Geranmayeh A, Seyyedsalehi SA (2010) Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks. Comput Methods Programs Biomed 100(3):237–247

    Article  Google Scholar 

  • Bazzoli A, Tettamanzi AG (2004) A memetic algorithm for protein structure prediction in a 3D-lattice HP model. Workshops on applications of evolutionary computation. Springer, Berlin, pp 1–10

    Google Scholar 

  • Berger B, Leighton T (1998) Protein folding in the hydrophobic–hydrophilic (HP) model is NP-complete. J Comput Biol 5(1):27–40

    Article  Google Scholar 

  • Bujnicki JM (2006) Protein-structure prediction by recombination of fragments. ChemBioChem 7(1):19–27

    Article  Google Scholar 

  • Chen W, Ding H, Feng P, Lin H, Chou KC (2016) iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7(13):16895

    Google Scholar 

  • Crescenzi P, Goldman D, Papadimitriou C, Piccolboni A, Yannakakis M (1998) On the complexity of protein folding. J Comput Biol 5(3):423–465

    Article  MATH  Google Scholar 

  • Custódio FL, Barbosa HJ, Dardenne LE (2014) A multiple minima genetic algorithm for protein structure prediction. Appl Soft Comput 15:88–99

    Article  Google Scholar 

  • Cutello V, Nicosia G, Pavone M, Timmis J (2007) An immune algorithm for protein structure prediction on lattice models. IEEE Trans Evol Comput 11(1):101–117

    Article  Google Scholar 

  • Davis IW, Baker D (2009) RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol 385(2):381–392

    Article  Google Scholar 

  • De Araújo AFP (1999) Folding protein models with a simple hydrophobic energy function: the fundamental importance of monomer inside/outside segregation. Proc Nat Acad Sci 96(22):12482–12487

    Article  Google Scholar 

  • Lau KF, Dill KA (1989) A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules 22(10):3986–3997

    Article  Google Scholar 

  • Dill KA, Fiebig KM, Chan HS (1993) Cooperativity in protein-folding kinetics. Proc Nat Acad Sci 90(5):1942–1946

    Article  Google Scholar 

  • Dobson CM, Šali A, Karplus M (1998) Protein folding: a perspective from theory and experiment. Angew Chem Int Ed 37(7):868–893

    Article  Google Scholar 

  • Do DD (2017) A novel and efficient ant colony optimization algorithm for protein 3D structure prediction. VNU-UET technical report

  • Garza-Fabre M, Rodriguez-Tello E, Toscano-Pulido G (2015) Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction. Comput Oper Res 53:128–153

    Article  MathSciNet  MATH  Google Scholar 

  • Guntert P (2004) Automated NMR structure calculation with cyana. Protein NMR Tech 278:353–378

    Article  Google Scholar 

  • Jana ND, Sil J, Das S (2017) An improved harmony search algorithm for protein structure prediction using 3D off-lattice model. International conference on harmony search algorithm. Springer, Singapore, pp 304–314

    Chapter  Google Scholar 

  • Kanj F, Mansour N, Khachfe H, Abu-Khzam F (2009) Protein structure prediction in the 3D HP model. In IEEE/ACS international conference on computer systems and applications, 2009. AICCSA 2009. IEEE. pp 732–736

  • Khaji E, Karami M, Garkani-Nejad Z (2016) 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction. J Theor Biol 391:81–87

    Article  Google Scholar 

  • Khimasia MM, Coveney PV (1997) Protein structure prediction as a hard optimization problem: the genetic algorithm approach. Mol Simul 19(4):205–226

    Article  Google Scholar 

  • Larranaga P (2002) A review on estimation of distribution algorithms. Estimation of distribution algorithms. Springer, New York, pp 57–100

    Chapter  Google Scholar 

  • Lee SY, Lee JY, Jung KS, Ryu KH (2009) A 9-state hidden Markov model using protein secondary structure information for protein fold recognition. Comput Biol Med 39(6):527–534

    Article  Google Scholar 

  • Liu J, Sun Y, Li G, Song B, Huang W (2013) Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins. Comput Biol Chem 47:142–148

    Article  Google Scholar 

  • Mansour N, Kanj F, Khachfe H (2010) Evolutionary algorithm for protein structure prediction. In: 2010 sixth international conference on natural computation (ICNC), vol 8. IEEE, pp 3974–3977

  • Patton AL, Punch III WF, Goodman ED (1995) A standard GA approach to native protein conformation prediction. In: ICGA, pp 574–581

  • Raman S, Huang YJ, Mao B, Rossi P, Aramini JM, Liu G, Montelione GT, Baker D (2010) Accurate automated protein NMR structure determination using unassigned NOESY data. J Am Chem Soc 132(1):202–207

    Article  Google Scholar 

  • Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Soft Comput 13(5):2837–2856

    Article  Google Scholar 

  • Razmara J, Deris SB, Parvizpour S (2013) A rapid protein structure alignment algorithm based on a text modeling technique. Bioinformation 6(9):344

    Article  Google Scholar 

  • Santos J, Diéguez M (2011) Differential evolution for protein structure prediction using the HP model. International work-conference on the interplay between natural and artificial computation. Springer, Berlin, pp 323–333

    Google Scholar 

  • Shen HB, Yang J, Liu XJ, Chou KC (2005) Using supervised fuzzy clustering to predict protein structural classes. Biochem Biophys Res Commun 334(2):577–581

    Article  Google Scholar 

  • Shen Y, Vernon R, Baker D, Bax A (2009) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43(2):63–78

    Article  Google Scholar 

  • Shmygelska A, Hoos HH (2005) An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinform 6(1):30

    Article  Google Scholar 

  • Spencer M, Eickholt J, Cheng J (2015) A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinf 12(1):103–112

    Article  Google Scholar 

  • Storm CN, Lyngsø RB (1999) Protein folding in the 2D HP model. Tech rep, Technical Report RS-99-16 BRICS, University of Aarhus, Denmark

  • Sudha S, Baskar S, Amali SMJ, Krishnaswamy S (2015) Protein structure prediction using diversity controlled self-adaptive differential evolution with local search. Soft Comput 19(6):1635–1646

    Article  Google Scholar 

  • Toma L, Toma S (1996) Contact interactions method: a new algorithm for protein folding simulations. Protein Sci 5(1):147–153

    Article  Google Scholar 

  • Unger R, Moult J (1993) Genetic algorithms for protein folding simulations. J Mol Biol 231(1):75–81

    Article  Google Scholar 

  • Wang Y, Mao H, Yi Z (2017) Protein secondary structure prediction by using deep learning method. Knowl-Based Syst 118:115–123

    Article  Google Scholar 

  • Xie S, Li Z, Hu H (2018) Protein secondary structure prediction based on the fuzzy support vector machine with the hyperplane optimization. Gene 642:74–83

    Article  Google Scholar 

  • Yue K, Fiebig KM, Thomas PD, Chan HS, Shakhnovich EI, Dill KA (1995) A test of lattice protein folding algorithms. Proc Nat Acad Sci 92(1):325–329

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematical Sciences, University of Tabriz, Tabriz, Iran

    Amir Morshedian, Jafar Razmara & Shahriar Lotfi

Authors
  1. Amir Morshedian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jafar Razmara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahriar Lotfi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jafar Razmara.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Communicated by V. Loia.

Appendix: TEST DATA

Appendix: TEST DATA

See Tables 9, 10, and 11.

Table 9 48 Peptide length test cases
Full size table
Table 10 64 peptide length test cases
Full size table
Table 11 Different length test cases
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morshedian, A., Razmara, J. & Lotfi, S. A novel approach for protein structure prediction based on an estimation of distribution algorithm. Soft Comput 23, 4777–4788 (2019). https://doi.org/10.1007/s00500-018-3130-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3130-0

Keywords

Search

Navigation