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.
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References
Bajaj C, Chowdhury R, Siddavanahalli V (2011) F2Dock: fast Fourier protein–protein docking. IEEE/ACM Trans Comput Biol Bioinf 8(1):45–58
Banting L, Clark T, Thurston DE (2012) Drug design strategies: computational techniques and applications, 1st edn. Royal Society of Chemistry, London
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):367–382
Cai Y, Wang J, Yin J (2012) Learning-enhanced differential evolution for numerical optimization. Soft Comput 16(2):303–330
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):1893–1910
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
Chen R, Li L, Weng Z (2003) Zdock: an initial-stage protein-docking algorithm. Proteins 52(1):80–87
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):248–259
Clark KP (1995) Flexible ligand docking without parameter adjustment across four ligand–receptor complexes. J Comput Chem 16(10):1210–1226
Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16(3):193–203
Correlation (2016) https://en.wikipedia.org/wiki/Matthews_correlation_coefficient. Accessed 13 June 2016
de Vries S, Zacharias M (2013) Flexible docking and refinement with a coarse-grained protein model using ATTRACT. Proteins 81(12):2167–2174
de Vries SJ, van Dijk M, Bonvin AM (2010) The HADDOCK web server for data-driven biomolecular docking. Nat Protoc 5:883–897
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):3–18
dssp (2012) Centre for Molecular and Biomolecular Informatics. http://swift.cmbi.ru.nl/gv/dssp. Accessed 08 Feb 2012
Esquivel-Rodríguez J, Kihara D (2012) Effect of conformation sampling strategies in genetic algorithm for multiple protein docking. BMC Proc 6(Suppl 7):S4
Esquivel-Rodriguez J, Yang YD, Kihara D (2012) Multi-LZerD: multiple protein docking for asymmetric complexes. Proteins 80(7):1818–1833
Fernandez-Recio J, Totrov M, Abagyan R (2003) ICM-DISCO docking by global energy optimization with fully flexible side-chains. Proteins 52(1):113–117
Gabb HA, Jackson RM, Sternberg MJ (1997) Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 272(1):106–120
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):2544–2551
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):281–299
Hashmi I, Shehu A (2012) HopDock: a probabilistic search algorithm for decoy sampling in protein–protein docking. Proteome Sci 11(Supplement):1
Huang P, Love JJ, Mayo SL (2005) Adaptation of a fast Fourier transform-based docking algorithm for protein design. J Comput Chem 26(12):1222–1232
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):727–748
Kong X, Ouyang H, Piao X (2013) A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization. Soft Comput 17(12):2293–2309
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 247–258
Kozakov D, Brenke R, Comeau SR, Vajda S (2006) PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65(2):392–406
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):2159–2166
Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule–ligand interactions. J Mol Biol 161(2):269–288
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
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
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–W449
Mashiach E, Nussinov R, Wolfson HJ (2010) FiberDock: flexible induced-fit backbone refinement in molecular docking. Proteins 78(6):1503–1519
Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32
Moal IH, Bates PA (2010) SwarmDock and the use of normal modes in protein–protein docking. Int J Mol Sci 1(10):3623–3648
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):1639–1662
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):1656–1676
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):934–946
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):1771–1773
Protein Docking Benchmark—Zlab (2010) https://zlab.umassmed.edu/benchmark/. Accessed 16 Sep 2010
Pymol (2000) http://pldserver1.biochem.queensu.ca/~rlc/work/teaching/BCHM823/pymol/alignment/. Accessed 20 Nov 2000
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Reid DJ (1996) Genetic algorithms in constrained optimization. Math Comput Model 23(5):87–111
Ritchie DW, Kozakov D, Vajda S (2008) Accelerating and focusing protein–protein docking correlations using multi-dimensional rotational FFT generating functions. Bioinformatics 24(17):1865–1873
Roberts VA, Thompson EE, Pique ME, Perez MS, Ten Eyck LF (2013) DOT2: macromolecular docking with improved biophysical models. J Comput Chem 34(20):1743–1758
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367
Storn R, Price KV (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
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):2371–2385
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):1635–1646
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
Takahama T, Sakai S (2009) Solving difficult constrained optimization problems by the ε constrained differential evolution with gradient-based mutation. Constr Handl Evol Optim 198:51–72
Thomsen R, Christensen MH (2006) MolDock: a new technique for high accuracy molecular docking. J Med Chem 49(11):3315–3321
Tovchigrechko A, Vakser IA (2005) Development and testing of an automated approach to protein docking. Proteins 60(2):296–301
Tovchigrechko A, Vakser IA (2006) GRAMM-X public web server for protein–protein docking. Nucleic Acids Res 34:W310–W314
Wang C, Bradley P, Baker D (2007) Protein–protein docking with backbone flexibility. J Mol Biol 373(2):503–519
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.
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Sudha, S., Baskar, S. & Krishnaswamy, S. Protein docking using constrained self-adaptive differential evolution algorithm. Soft Comput 23, 11651–11669 (2019). https://doi.org/10.1007/s00500-018-03717-2
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DOI: https://doi.org/10.1007/s00500-018-03717-2