Abstract
This work presents four hybrid methods based on the Self-adaptive Differential Evolution algorithm with fragment insertion applied to the protein structure prediction problem. The protein representation is the backbone torsion angles with side chain centroid coordinates. The fragment insertion is made by the Monte Carlo algorithm. The hybrid methods were compared with recent and compatible methods from the literature, where two proposed approaches achieved competitive results. The results have shown that using parameter control and fragment insertion greatly improves the results of the prediction when compared to fragment-less methods or without parameter control. Furthermore, an extra analysis was conducted using GDT-TS and TM-Score metrics to better understand the results obtained.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Berger, B., Leighton, T.: Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. J. Comput. Biol. 5(1), 27–40 (1998)
Berman, H.M., et al.: The protein data bank. Acta Crystallogr. Sect. D: Biol. Crystallogr. 58(6), 899–907 (2002)
Borguesan, B., e Silva, M.B., Grisci, B., Inostroza-Ponta, M., Dorn, M.: APL: an angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction. Comput. Biol. Chem. 59, 142–157 (2015)
Buchan, D.W., Minneci, F., Nugent, T.C., Bryson, K., Jones, D.T.: Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic Acids Res. 41(W1), W349–W357 (2013)
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)
Drenth, J.: Principles of Protein X-Ray Crystallography. Springer, New York (2007). https://doi.org/10.1007/0-387-33746-6
Garza-Fabre, M., Kandathil, S.M., Handl, J., Knowles, J., Lovell, S.C.: Generating, maintaining, and exploiting diversity in a memetic algorithm for protein structure prediction. Evol. Comput. 24(4), 577–607 (2016)
Habeck, M., Nilges, M., Rieping, W.: Replica-exchange Monte Carlo scheme for Bayesian data analysis. Phys. Rev. Lett. 94(1), 018105 (2005)
Hart, W.E., Istrail, S.: Robust proofs of NP-hardness for protein folding: general lattices and energy potentials. J. Comput. Biol. 4(1), 1–22 (1997)
Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015)
Liu, J., Li, G., Yu, J., Yao, Y.: Heuristic energy landscape paving for protein folding problem in the three-dimensional HP lattice model. Comput. Biol. Chem. 38, 17–26 (2012)
Lopes, H.S.: Evolutionary algorithms for the protein folding problem: a review and current trends. In: Smolinski, T.G., Milanova, M.G., Hassanien, A.E. (eds.) Computational Intelligence in Biomedicine and Bioinformatics. SCI, vol. 151, pp. 297–315. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70778-3_12
Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A.: Critical assessment of methods of protein structure prediction: progress and new directions in round XI. Proteins: Struct. Funct. Bioinf. 84(S1), 4–14 (2016)
Narloch, P.H., Parpinelli, R.S.: The protein structure prediction problem approached by a cascade differential evolution algorithm using ROSETTA, pp. 294–299. IEEE (2017)
Nunes, L.F., Galvão, L.C., Lopes, H.S., Moscato, P., Berretta, R.: An integer programming model for protein structure prediction using the 3D-HP side chain model. Discrete Appl. Math. 198, 206–214 (2016)
de Oliveira, S.H., Shi, J., Deane, C.M.: Building a better fragment library for de novo protein structure prediction. PloS One 10(4), e0123998 (2015)
Parpinelli, R.S., Lopes, H.S.: New inspirations in swarm intelligence; a survey. Int. J. Bio-Inspir. Comput. 3(1), 1–16 (2011)
Parpinelli, R.S., Plichoski, G.F., Da Silva, R.S., Narloch, P.H.: A review of technique for on-line control of parameters in swarm intelligence and evolutionary computation algorithms. Int. J. Bio-Inspir. Comput. (IJBIC) (2019, accepted for publication)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-31306-0
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)
Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791. IEEE (2005)
Silva, R.S., Parpinelli, R.S.: A multistage simulated annealing for protein structure prediction using Rosetta. In: Anais do Computer on the Beach, pp. 850–859 (2018)
Vanderbilt, D., Louie, S.G.: A Monte Carlo simulated annealing approach to optimization over continuous variables. J. Comput. Phys. 56(2), 259–271 (1984)
Walsh, G.: Proteins: Biochemistry and Biotechnology. Wiley, Hoboken (2002)
Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)
Xu, J., Zhang, Y.: How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 26(7), 889–895 (2010)
Zemla, A.: LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 31(13), 3370–3374 (2003)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)
Zhang, X., et al.: 3D protein structure prediction with genetic tabu search algorithm. BMC Syst. Biol. 4(1), S6 (2010)
Zhang, Y., Skolnick, J.: Scoring function for automated assessment of protein structure template quality. Proteins: Struct. Funct. Bioinf. 57(4), 702–710 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, R.S., Stubs Parpinelli, R. (2019). A Self-adaptive Differential Evolution with Fragment Insertion for the Protein Structure Prediction Problem. In: Blesa Aguilera, M., Blum, C., Gambini Santos, H., Pinacho-Davidson, P., Godoy del Campo, J. (eds) Hybrid Metaheuristics. HM 2019. Lecture Notes in Computer Science(), vol 11299. Springer, Cham. https://doi.org/10.1007/978-3-030-05983-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-05983-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05982-8
Online ISBN: 978-3-030-05983-5
eBook Packages: Computer ScienceComputer Science (R0)