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Protein docking using constrained self-adaptive differential evolution algorithm

  • S. SudhaEmail author
  • S. Baskar
  • S. Krishnaswamy
Methodologies and Application
  • 19 Downloads

Abstract

The objective of protein docking is to achieve a relative orientation and an optimized conformation between two proteins that results in a stable structure with the minimized potential energy. Constrained self-adaptive differential evolution (Cons_SaDE) algorithm is used to find the minimum energy conformation using proposed constraints such as boundary surface complementary interactions, non-bonded inter-atomic allowed distances and finding of interaction and non-interaction sites. With these constraints, Cons_SaDE is efficient enough to explore the promising solutions by gradually self-adapting the strategies and parameters learned from their previous experiences. Modified sampling scheme called rotate only representation is used to represent a docking conformation. GROMOS53A6 force field is used to find the potential energy. To test the performance of this algorithm, few bound and unbound complexes from Protein Data Bank (PDB) and few easy, medium and difficult complexes from Zlab Benchmark 4.0 are used. Buried surface area, root-mean-square deviation (RMSD) and correlation coefficient are some of the metrics applied to evaluate the best docked conformations. RMSD values of the best docked conformations obtained from five popular docking Web servers are compared with Cons_SaDE results, and nonparametric statistical tests for multiple comparisons with control method are implemented to show the performance of this algorithm. Cons_SaDE has produced good-quality solutions for the most of the data sets considered.

Keywords

Constrained self-adaptive differential evolution GROMOS 53A6 force field Matthew’s correlation coefficient Protein docking Rotate only representation 

Notes

Acknowledgements

The first author takes this opportunity to express her profound gratitude and deep regards to Ms. P.J. Eswari Pandaranayaka, Postdoctoral Research Scholar, MKU, for her exemplary support by providing valuable information and guidance and constructive feedback on the evaluation of the results of this work. The first author is obliged to Mrs. C.V. Nisha Angeline, Asst. Prof, I.T, for her assistance in initial coding. The first author is thankful to Mr. G. Vivek, Software Engineer, Ericsson, for his constant support by means of facilitating the cluster installation and debugging, essential for this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest

Ethical approval

This study does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Bajaj C, Chowdhury R, Siddavanahalli V (2011) F2Dock: fast Fourier protein–protein docking. IEEE/ACM Trans Comput Biol Bioinf 8(1):45–58Google Scholar
  2. Banting L, Clark T, Thurston DE (2012) Drug design strategies: computational techniques and applications, 1st edn. Royal Society of Chemistry, LondonGoogle Scholar
  3. Baxter CA, Murray CW, Clark DE, Westhead DR, Eldridge MD (1998) Flexible docking using Tabu search and an empirical estimate of binding affinity. Proteins 33(3):367–382Google Scholar
  4. Cai Y, Wang J, Yin J (2012) Learning-enhanced differential evolution for numerical optimization. Soft Comput 16(2):303–330Google Scholar
  5. Cai X, Hu Z, Fan Z (2013) A novel memetic algorithm based on invasive weed optimization and differential evolution for constrained optimization. Soft Comput 17(10):1893–1910Google Scholar
  6. Chaudhury S, Gray JJ (2008) Conformer selection and induced fit in flexible backbone protein–protein docking using computational and NMR ensembles. J Mol Biol 381(4):1068–1087.  https://doi.org/10.1016/j.jmb.2008.05.042 Google Scholar
  7. Chen R, Li L, Weng Z (2003) Zdock: an initial-stage protein-docking algorithm. Proteins 52(1):80–87Google Scholar
  8. Chen K, Li T, Cao T (2006) Tribe-PSO: a novel global optimization algorithm and its application in molecular docking. J Chemometr Intell Lab Syst 82(1):248–259Google Scholar
  9. Clark KP (1995) Flexible ligand docking without parameter adjustment across four ligand–receptor complexes. J Comput Chem 16(10):1210–1226Google Scholar
  10. Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203Google Scholar
  11. Correlation (2016) https://en.wikipedia.org/wiki/Matthews_correlation_coefficient. Accessed 13 June 2016
  12. de Vries S, Zacharias M (2013) Flexible docking and refinement with a coarse-grained protein model using ATTRACT. Proteins 81(12):2167–2174Google Scholar
  13. de Vries SJ, van Dijk M, Bonvin AM (2010) The HADDOCK web server for data-driven biomolecular docking. Nat Protoc 5:883–897Google Scholar
  14. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18Google Scholar
  15. dssp (2012) Centre for Molecular and Biomolecular Informatics. http://swift.cmbi.ru.nl/gv/dssp. Accessed 08 Feb 2012
  16. Esquivel-Rodríguez J, Kihara D (2012) Effect of conformation sampling strategies in genetic algorithm for multiple protein docking. BMC Proc 6(Suppl 7):S4Google Scholar
  17. Esquivel-Rodriguez J, Yang YD, Kihara D (2012) Multi-LZerD: multiple protein docking for asymmetric complexes. Proteins 80(7):1818–1833Google Scholar
  18. Fernandez-Recio J, Totrov M, Abagyan R (2003) ICM-DISCO docking by global energy optimization with fully flexible side-chains. Proteins 52(1):113–117Google Scholar
  19. Gabb HA, Jackson RM, Sternberg MJ (1997) Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 272(1):106–120Google Scholar
  20. Garzon JI, Lopéz-Blanco JR, Pons C, Kovacs J, Abagyan R, Fernandez-Recio J, Chacon P (2009) FRODOCK: a new approach for fast rotational protein–protein docking. Bioinformatics 25(9):2544–2551Google Scholar
  21. Gray JJ, Moughan SE, Wang C, Schueler-Furman O, Kuhlman B, Rohl CA, Baker D (2003) Protein–protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J Mol Biol 331(1):281–299Google Scholar
  22. Hashmi I, Shehu A (2012) HopDock: a probabilistic search algorithm for decoy sampling in protein–protein docking. Proteome Sci 11(Supplement):1Google Scholar
  23. Huang P, Love JJ, Mayo SL (2005) Adaptation of a fast Fourier transform-based docking algorithm for protein design. J Comput Chem 26(12):1222–1232Google Scholar
  24. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748Google Scholar
  25. Kong X, Ouyang H, Piao X (2013) A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization. Soft Comput 17(12):2293–2309Google Scholar
  26. Korb O, Stutzle T, Exner TE (2006) PLANTS: application of ant colony optimization to structure-based drug design. In: Proceedings of ant colony optimization and swarm intelligence, 5th international workshop, pp 247–258Google Scholar
  27. Kozakov D, Brenke R, Comeau SR, Vajda S (2006) PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65(2):392–406Google Scholar
  28. Kozakov D, Beglov D, Bohnuud T, Mottarella S, Xia B, Hall DR, Vajda S (2013) How good is automated protein docking? Proteins Struct Funct Bioinform 81(12):2159–2166Google Scholar
  29. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule–ligand interactions. J Mol Biol 161(2):269–288Google Scholar
  30. Li B, Kihara D (2012) Protein docking prediction using predicted protein–protein interface. BMC Bioinformatics 13(7):1–17.  https://doi.org/10.1186/1471-2105-13-7 Google Scholar
  31. Li L, Guo D, Huang Y, Liu S, Xiao Y (2011) ASPDock: protein–protein docking algorithm using atomic solvation parameters model. BMC Bioinform 12:36.  https://doi.org/10.1186/1471-2105-12-36 Google Scholar
  32. Macindoe G, Mavridis L, Venkatraman V, Devignes MD, Ritchie DW (2010) HexServer: an FFT-based protein docking server powered by graphics processors. Nucleic Acids Res 38:W445–W449Google Scholar
  33. Mashiach E, Nussinov R, Wolfson HJ (2010) FiberDock: flexible induced-fit backbone refinement in molecular docking. Proteins 78(6):1503–1519Google Scholar
  34. Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32Google Scholar
  35. Moal IH, Bates PA (2010) SwarmDock and the use of normal modes in protein–protein docking. Int J Mol Sci 1(10):3623–3648Google Scholar
  36. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and empirical binding free energy function. J Comput Chem 19(14):1639–1662Google Scholar
  37. Oostenbrink C, Villa A, Mark AE, Van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. Wiley J Comput Chem 25(13):1656–1676Google Scholar
  38. Pei J, Wang Q, Liu Z, Li Q, Yang KL, Lai L (2006) PSI-DOCK: towards highly efficient and accurate flexible ligand docking. Proteins 62(4):934–946Google Scholar
  39. Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, Weng Z (2014) ZDOCK Server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics 30(12):1771–1773Google Scholar
  40. Protein Docking Benchmark—Zlab (2010) https://zlab.umassmed.edu/benchmark/. Accessed 16 Sep 2010
  41. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417Google Scholar
  42. Reid DJ (1996) Genetic algorithms in constrained optimization. Math Comput Model 23(5):87–111MathSciNetzbMATHGoogle Scholar
  43. Ritchie DW, Kozakov D, Vajda S (2008) Accelerating and focusing protein–protein docking correlations using multi-dimensional rotational FFT generating functions. Bioinformatics 24(17):1865–1873Google Scholar
  44. Roberts VA, Thompson EE, Pique ME, Perez MS, Ten Eyck LF (2013) DOT2: macromolecular docking with improved biophysical models. J Comput Chem 34(20):1743–1758Google Scholar
  45. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367Google Scholar
  46. Storn R, Price KV (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359MathSciNetzbMATHGoogle Scholar
  47. Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG (2008) Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening. J Chem Inf Model 48(12):2371–2385Google Scholar
  48. 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):1635–1646Google Scholar
  49. Suenaga A, Okimoto N, Hirano Y, Fukui K (2012) An efficient computational method for calculating ligand binding affinities. PLoS ONE 7(8):e42846.  https://doi.org/10.1371/journal.pone.0042846 Google Scholar
  50. Takahama T, Sakai S (2009) Solving difficult constrained optimization problems by the ε constrained differential evolution with gradient-based mutation. Constr Handl Evol Optim 198:51–72Google Scholar
  51. Thomsen R, Christensen MH (2006) MolDock: a new technique for high accuracy molecular docking. J Med Chem 49(11):3315–3321Google Scholar
  52. Tovchigrechko A, Vakser IA (2005) Development and testing of an automated approach to protein docking. Proteins 60(2):296–301Google Scholar
  53. Tovchigrechko A, Vakser IA (2006) GRAMM-X public web server for protein–protein docking. Nucleic Acids Res 34:W310–W314Google Scholar
  54. Wang C, Bradley P, Baker D (2007) Protein–protein docking with backbone flexibility. J Mol Biol 373(2):503–519Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Thiagarajar College of EngineeringMaduraiIndia
  2. 2.The Institute of Mathematical SciencesChennaiIndia

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