A Multi-objective Swarm-Based Algorithm for the Prediction of Protein Structures

  • Leonardo de Lima Corrêa
  • Márcio DornEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11538)


The protein structure prediction is one of the most challenging problems in Structural Bioinformatics. In this paper, we present some variations of the artificial bee colony algorithm to deal with the problem’s multimodality and high-dimensionality by introducing multi-objective optimization and knowledge from experimental proteins through the use of protein contact maps. Obtained results regarding measures of structural similarity indicate that our approaches surpassed their previous ones, showing the real need to adapt the method to tackle the problem’s complexities.


Swarm intelligence Multi-objective optimization PSP 



This work was supported by grants from FAPERGS [16/2551-0000520-6], MCT/CNPq [311022/2015-4; 311611/2018-4], CAPES-STIC AMSUD [88887.135130/2017-01] - Brazil, Alexander von Humboldt-Stiftung (AvH) [BRA 1190826 HFST CAPES-P] - Germany. This study was financed in part by CAPES - Finance Code 001.


  1. 1.
    Abriata, L.A., Tamò, G.E., Monastyrskyy, B., Kryshtafovych, A., Dal Peraro, M.: Assessment of hard target modeling in CASP12 reveals an emerging role of alignment-based contact prediction methods. Proteins: Struct. Funct. Bioinf. 86, 97–112 (2018)CrossRefGoogle Scholar
  2. 2.
    Adhikari, B., Hou, J., Cheng, J.: Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning. Proteins: Struct. Funct. Bioinf. 86, 84–96 (2018)CrossRefGoogle Scholar
  3. 3.
    Akay, B., Karaboga, D.: A modified artificial bee colony algorithm for real-parameter optimization. Inf. Sci. 192, 120–142 (2012)CrossRefGoogle Scholar
  4. 4.
    Borguesan, B., Inostroza, M., Dorn, M.: NIAS-server: neighbors influence of amino acids and secondary structures in proteins. J. Comput. Biol. 24, 255–265 (2016)CrossRefGoogle Scholar
  5. 5.
    Corrêa, L.D.L., Dorn, M.: A knowledge-based artificial bee colony algorithm for the 3-D protein structure prediction problem. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, July 2018Google Scholar
  6. 6.
    Cutello, V., Narzisi, G., Nicosia, G.: A multi-objective evolutionary approach to the protein structure prediction problem. J. R. Soc. Interface 3(6), 139–151 (2006)CrossRefGoogle Scholar
  7. 7.
    Dorn, M., e Silva, M.B., Buriol, L.S., Lamb, L.C.: Three-dimensional protein structure prediction: methods and computational strategies. Comput. Biol. Chem. 53, 251–276 (2014)CrossRefGoogle Scholar
  8. 8.
    Gao, W., Liu, S., Huang, L.: A global best artificial bee colony algorithm for global optimization. J. Comput. Appl. Math. 236(11), 2741–2753 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Handl, J., Lovell, S.C., Knowles, J.: Investigations into the effect of multiobjectivization in protein structure prediction. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 702–711. Springer, Heidelberg (2008). Scholar
  10. 10.
    Jones, D.T., Singh, T., Kosciolek, T., Tetchner, S.: MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics 31(7), 999–1006 (2014)CrossRefGoogle Scholar
  11. 11.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kim, D.E., DiMaio, F., Yu-Ruei Wang, R., Song, Y., Baker, D.: One contact for every twelve residues allows robust and accurate topology-level protein structure modeling. Proteins: Struct. Funct. Bioinf. 82, 208–218 (2014)CrossRefGoogle Scholar
  13. 13.
    Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006)CrossRefGoogle Scholar
  14. 14.
    Li, G., Niu, P., Xiao, X.: Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl. Soft Comput. 12(1), 320–332 (2012)CrossRefGoogle Scholar
  15. 15.
    Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A.: Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins: Struct. Funct. Bioinf. 86, 7–15 (2018)CrossRefGoogle Scholar
  16. 16.
    Olson, B., Shehu, A.: Multi-objective optimization techniques for conformational sampling in template-free protein structure prediction. In: International Conference on Bioinformatics and Computational Biology (2014)Google Scholar
  17. 17.
    Rohl, C.A., Strauss, C.E., Misura, K.M., Baker, D.: Protein structure prediction using Rosetta. Methods Enzymol. 383, 66–93 (2004)CrossRefGoogle Scholar
  18. 18.
    Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., Bonvin, A.M.: Assessment of contact predictions in CASP12: co-evolution and deep learning coming of age. Proteins: Struct. Funct. Bioinf. 86, 51–66 (2018)CrossRefGoogle Scholar
  19. 19.
    Unger, R., Moult, J.: Finding the lowest free energy conformation of a protein is an NP-hard problem. Bull. Math. Biol. 55(6), 1183–1198 (1993)CrossRefGoogle Scholar
  20. 20.
    Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)MathSciNetzbMATHGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of InformaticsFederal University of Rio Grande do SulPorto AlegreBrazil

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