Advertisement

Protein Folding Problem in the Case of Peptides Solved by Hybrid Simulated Annealing Algorithms

  • Anylu Melo-Vega
  • Juan Frausto-SolísEmail author
  • Guadalupe Castilla-Valdez
  • Ernesto Liñán-García
  • Juan Javier González-Barbosa
  • David Terán-Villanueva
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 749)

Abstract

Protein Folding Problem (PFP) is a computational challenge with many implications in bioinformatics and computer science. This problem consists in determining the biological and functional three-dimensional structure of the atoms of the amino acid sequence of the protein, which is named Native Structure (NS). Whereas there are a huge number of possible structures this problem is classified as NP-Hard. For PFP, hybrid methods based on Simulated Annealing (SA) have been applied to different kinds of proteins with high-quality solutions. Nevertheless, at the time of presenting this work, they are not enough review of successful algorithms in the group of proteins called peptides. In addition, how successful are the algorithms applied to this kind of proteins has not been previously published. In this paper, we present the main variants of these methods, their applications, and the main characteristics of those algorithms that have made them successful in that area.

Keywords

Simulated annealing Protein folding problem Peptides Metropolis 

Notes

Acknowledgements

The authors would like to acknowledge to CONACYT, TECNM and PRODEP. An special acknowledgement to the Laboratorio Nacional de Tecnologías de la Información del Instituto Tecnológico de Ciudad Madero for the access to the cluster. This work has been partially supported by CONACYT Project 254498.

References

  1. 1.
    J.S. Richardson, in The Anatomy and Taxonomy of Protein Structure (1981), pp. 167–339Google Scholar
  2. 2.
    D. Osguthorpe, Ab initio protein folding. Curr. Opin. Struct. Biol. 10(2), 146–152 (2000)CrossRefGoogle Scholar
  3. 3.
    G.A. Khoury, J. Smadbeck, C.A. Kieslich, C.A. Floudas, Protein folding and de novo protein design for biotechnological applications. Trends Biotechnol. 32(2), 99–109 (2014)CrossRefGoogle Scholar
  4. 4.
    K.A. Dill, S.B. Ozkan, M.S. Shell, T.R. Weikl, The protein folding problem. Annu. Rev. Biophys. 37, 289–316 (2008)CrossRefGoogle Scholar
  5. 5.
    P. Crescenzi, D. Goldman, C. Papadimitriou, A. Piccolboni, M. Yannakakis, On the complexity of protein folding. J. Comput. Biol. 5(3), 423–465 (1998)CrossRefzbMATHGoogle Scholar
  6. 6.
    C. Levinthal, Are there pathways for protein folding? J. Chim. Phys. Physico-Chimie Biol. 65, 44–45 (1968)CrossRefGoogle Scholar
  7. 7.
    C.-I. Brändén, J. Tooze, in Introduction to Protein Structure (Garland Pub, 1999)Google Scholar
  8. 8.
    F.A. Momany, R.F. McGuire, A.W. Burgess, H.A. Scheraga, Energy parameters in polypeptides. VII. Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids. J. Phys. Chem. 79(22), 2361–2381 (1975)CrossRefGoogle Scholar
  9. 9.
    G.N. Ramachandran, C. Ramakrishnan, V. Sasisekharan, Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7(1), 95–99 (1963)CrossRefGoogle Scholar
  10. 10.
    L.B. Morales, R. Garduño-Juárez, D. Romero, Applications of simulated annealing to the multiple-minima problem in small peptides. J. Biomol. Struct. Dyn. 8(4), 721–735 (1991)CrossRefGoogle Scholar
  11. 11.
    Protein Structure Prediction Center. [Online]. Available: http://www.predictioncenter.org/index.cgi. Accessed 18 Mar 2017
  12. 12.
    J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, A. Tramontano, Critical assessment of methods of protein structure prediction: Progress and new directions in round XI. Proteins Struct. Funct. Bioinf. 84(S1), 4–14 (2016)CrossRefGoogle Scholar
  13. 13.
    Y. Zhang, I-TASSER server for protein 3D structure prediction. BMC BioinfGoogle Scholar
  14. 14.
    D.E. Kim, D. Chivian, D. Baker, Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 32(Web Server), W526–W531 (2004)Google Scholar
  15. 15.
    J. Soding, A. Biegert, A.N. Lupas, The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33(Web Server), W244–W248 (2005)Google Scholar
  16. 16.
    H. Zhou, S.B. Pandit, S.Y. Lee, J. Borreguero, H. Chen, L. Wroblewska, J. Skolnick, Analysis of TASSER-based CASP7 protein structure prediction results. Proteins Struct. Funct. Bioinf. 69(S8), 90–97 (2007)CrossRefGoogle Scholar
  17. 17.
    Z. Wang, J. Eickholt, J. Cheng, MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8. Bioinformatics 26(7), 882–888 (2010)CrossRefGoogle Scholar
  18. 18.
    J. Lundström, L. Rychlewski, J. Bujnicki, A. Elofsson, Pcons: a neural-network-based consensus predictor that improves fold recognition. Protein Sci. 10(11), 2354–2362 (2008)CrossRefGoogle Scholar
  19. 19.
    K. Karplus, SAM-T08, HMM-based protein structure prediction. Nucleic Acids Res. 37(Web Server), W492–W497 (2009)Google Scholar
  20. 20.
    K. Ginalski, A. Elofsson, D. Fischer, L. Rychlewski, 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19(8), 1015–1018 (2003)CrossRefGoogle Scholar
  21. 21.
    D. Jones, THREADER: protein sequence threading by double dynamic programming (1998), pp. 285–311Google Scholar
  22. 22.
    M. Källberg, H. Wang, S. Wang, J. Peng, Z. Wang, H. Lu, J. Xu, Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7(8), 1511–1522 (2012)CrossRefGoogle Scholar
  23. 23.
    A. Nayeem, J. Vila, H.A. Scheraga, A comparative study of the simulated-annealing and monte carlo-with-minimization approaches to the minimum-energy structures of polypeptides: [Met]-Enkephalin. J. Comput. Chem. 12(5), 594–605 (1991)Google Scholar
  24. 24.
    S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Sci. New Ser. 220(4598), 671–680 (1983)MathSciNetzbMATHGoogle Scholar
  25. 25.
    H. Zhou, J. Skolnick, J. Skolnick, V.S. Pande, M.B. Swindells, J.M. Thornton, J.J. Ward, K.M.S. Misura, D. Baker, Ab initio protein structure prediction using chunk-TASSER. Biophys. J. 93(5), 1510–1518 (2007)CrossRefGoogle Scholar
  26. 26.
    J. Frausto-Solis, E. Liñán-García, J.P. Sánchez-Hernández, J.J. González-Barbosa, C. González-Flores, G. Castilla-Valdez, Multiphase simulated annealing based on Boltzmann and Bose-Einstein distribution applied to protein folding problem. Adv. Bioinf. 2016, 7357123 (2016)CrossRefGoogle Scholar
  27. 27.
    F.P. Agostini, D.D.O. Soares-Pinto, M.A. Moret, C. Osthoff, P.G. Pascutti, Generalized simulated annealing applied to protein folding studies. J. Comput. Chem. 27(11), 1142–1155 (2006)CrossRefGoogle Scholar
  28. 28.
    L. Zhan, J.Z.Y. Chen, W.-K. Liu, Conformational study of Met-enkephalin based on the ECEPP force fields. Biophys. J. 91(7), 2399–2404 (2006)CrossRefGoogle Scholar
  29. 29.
    P. Fengbin, Z. Huilin, W. Yanjie, F. Shengzhong, Y. Zhixiang, Protein folding study based on parallel group annealing algorithms 4(5), 26–34 (2013)Google Scholar
  30. 30.
    J. Frausto-Solis, E. Román, Analytically tuned simulated annealing applied to the protein folding problem, in International Conference on Computational Science, 2007 (2007), pp. 370–377Google Scholar
  31. 31.
    Y. Sakae, T. Hiroyasu, M. Miki, K. Ishii, Y. Okamoto, Combination of genetic crossover and replica-exchange method for conformational search of protein systems (2015)Google Scholar
  32. 32.
    Y. Okamoto, Tackling the multiple-minima problem in protein folding by monte carlo simulated annealing and generalized-ensemble algorithms. Int. J. Mod. Phys. C 10(8), 1571–1582 (1999)CrossRefGoogle Scholar
  33. 33.
    T. Hiroyasu, M. Miki, S. Ogura, K. Aoi, T. Yoshida, Y. Okamoto, J. Dongarra, Energy minimization of protein tertiary structure by parallel simulated annealing using genetic crossover, in 2002 Genetic and Evolutionary Computation Conference (GECCO 2002) Workshop Program (2002), pp. 49–51Google Scholar
  34. 34.
    G.-F. Hao, W.-F. Xu, S.-G. Yang, G.-F. Yang, Multiple simulated annealing-molecular dynamics (MSA-MD) for conformational space search of peptide and miniprotein. Sci. Rep. 5, 15568 (2015)CrossRefGoogle Scholar
  35. 35.
    J. Frausto-Solis, J.P. Sánchez-Hernández, M. Sánchez-Pérez, E.L.L. García, Golden ratio simulated annealing for protein folding problem. Int. J. Comput. Methods 12(6), 1550037 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    J. Frausto-Solis, E. Liñáan-García, M. Sanchez-Perez, J.P. Sanchez-Hernandez, Chaotic multiquenching annealing applied to the protein folding problem. Sci. World J. 2014 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Anylu Melo-Vega
    • 1
  • Juan Frausto-Solís
    • 1
    Email author
  • Guadalupe Castilla-Valdez
    • 1
  • Ernesto Liñán-García
    • 2
  • Juan Javier González-Barbosa
    • 2
  • David Terán-Villanueva
    • 2
  1. 1.TecNM - Instituto Tecnológico de Cd. MaderoCiudad MaderoMexico
  2. 2.Universidad Autónoma de CoahuilaSaltilloMexico

Personalised recommendations