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Multi-Objective Approach for Protein Structure Prediction

  • S. Sudha
  • S. Baskar
  • S. Krishnaswamy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

This work proposes to optimize Protein Structure Prediction (PSP) using multi-objective ab initio approach. This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and Dynamic Crowding Distance (DCD) strategies in NSGA-II for PSP by minimizing free Potential Energy (PE) and minimizing Solvent Accessible Surface area (SAS). In this model, a trigonometric representation is used to compute backbone and side-chain torsion angles of protein atoms. Free energy is calculated using Chemistry at HARvard Macromolecular Mechanics (CHARMm -22). SAS is calculated using dssp program. Both objectives together evaluate the structures of protein conformations. The evolution of protein conformations is directed by optimization of protein energy and surface area contributions using MNSGA-II. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization (RGA) with weighted sum of objectives. TOPSIS technique is applied on obtained non-dominated solutions to determine Best Compromise Solution (BCS). Result of MNSGA-II is compared with NSGA-II. The proposed model is validated with Met-enkephalin, a benchmark protein, obtaining very promising results.

Keywords

Protein Structure Prediction Free Potential Energy Solvent Accessible Surface Area modified NSGA-II Pareto-front Best Compromise Solution 

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References

  1. 1.
  2. 2.
    Cui, Y., Chen, R.S., Hung, W.: Protein folding simulation with genetic algorithm and super secondary structure constraints. Proteins: Structure, Function and Genetics 31, 247–257 (1998)CrossRefGoogle Scholar
  3. 3.
    Unger, R., Moult, J.: Genetic algorithms for protein folding simulations. Biochim. Biophys. 231, 75–81 (1993)Google Scholar
  4. 4.
    Hoque, T., Chetty, M., Sattar, A.: Extended hp model for protein structure prediction. J. Comput. Biol. 16(1), 85–103 (2009)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers (2002)Google Scholar
  6. 6.
    Judya, M., Ravichandrana, K., Murugesan, K.: A multi-objective evolutionary algorithm for protein structure prediction with immune operators. Comp. Methods in Biomechanics and Biomedical Engineering 12(4), 407–413 (2009)CrossRefGoogle Scholar
  7. 7.
    Calvo, J.C., Ortega, J.: Parallel protein structure prediction by multiobjective optimization. Parallel, Distributed and Network-based Processing 12(4), 407–413 (2009)Google Scholar
  8. 8.
    Shi, S.Y.M., Suganthan, N.: Parallel protein structure prediction by multiobjective optimization. KanGAL Report 2004007, 1–7 (2004)Google Scholar
  9. 9.
    Cutello, V., Narzisi, G., Nicosia, G.: A multi-objective evolutionary approach to the protein structure prediction problem: J. R. Soc. Interface 3, 139–151 (2006)CrossRefGoogle Scholar
  10. 10.
    Chamorro, A.E.M., Divina, F., Aguilar-Ruiz, J.S., Cortés, G.A.: A multi-objective genetic algorithm for the Protein Structure Prediction. In: Intelligent Systems Design and Applications ISDA 2011, pp. 1086–1090 (2011)Google Scholar
  11. 11.
    NIH Center for Macromolecular Modeling, & Bioinformatics, http://www.ks.uiuc.edu/
  12. 12.
    Wodak, S.J., Janin, J.: Analytical approximation to the accessible surface area of proteins. Proc. Nati. Acad. Sci. USA 77(4), 1736–1740 (1980)CrossRefGoogle Scholar
  13. 13.
    Centre for Molecular and Biomolecular Informatics, http://swift.cmbi.ru.nl/gv/dssp
  14. 14.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  15. 15.
    Luo, B., Zheng, J., Xie, J., Wu, J.: Dynamic crowding distance – a new diversity maintenance strategy for MOEAs. In: Proceedings of the IEEE International Conference on Natural Computation, pp. 580–585 (2008)Google Scholar
  16. 16.
    Jeyadevi, S., Baskar, S., Babulal, C.K., Willjuice Iruthayarajan, M.: Solving multiobjective optimal reactive power dispatch using modified NSGA-II. Int. J of Electrical Power and Energy Systems 33(2), 219–228 (2011)CrossRefGoogle Scholar
  17. 17.
    Kannan, S., Baskar, S., Mccalley, J.D., Murugan, P.: Application of NSGA-II algorithm to generation expansion planning. IEEE Trans. Power System 24(1), 454–461 (2009)CrossRefGoogle Scholar
  18. 18.
    Deb, K., Tewari, R., Dixit, M., Dutta, J.: Finding trade-off solutions close to KKT points using evolutionary multi-objective optimization. IIT Kanpur, KanGAL Report Number 2007006 (2007)Google Scholar
  19. 19.
    Biological Macromolecular Resource, Protein Data Bank, http://www.pdb.org/
  20. 20.
    Li, Z., Scheraga, H.A.: Structure and free energy of complex thermodynamic systems. Journal of Molecular Structures 179, 333 (1988)CrossRefGoogle Scholar
  21. 21.
    An open-source molecular visualization system, http://www.pymol.org/

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • S. Sudha
    • 1
  • S. Baskar
    • 1
  • S. Krishnaswamy
    • 2
  1. 1.Thiagarajar College of EngineeringMaduraiIndia
  2. 2.Centre of Excellence in BioinformaticsMadurai Kamaraj UniversityMaduraiIndia

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