Protein–ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein–ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein–ligand docking. We consider four search methods—a tradition genetic algorithm, LGA, SODOCK, and EDGA—and compare their performance in docking of six protein–ligand docking problems. The results show that EDGA is the most stable, reliable, and successful.
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References
1.
BermanH., BattistuzT., BhatT.N., et al.2002. The protein data bank. Acta Crystallogr. D Biol. Crystallogr., 58, 899–907.
2.
BermanH.M., WestbrookJ., FengZ., et al.2000. The protein data bank. Nucleic Acids Res. 28, 235–242.
3.
BharathamN., BharathamK., and ShelatA.A., et al.2014. Ligand binding more prediction by docking: mdm2/mdmx inhibitors as a case study. J. Chem. Inf. Model, 54, 648–659.
4.
BlumC., PuchingerJ., RaidlG.R., et al.2011. Hybrid metaheuristics in combinatorial optimization: A survey. Appl. Soft. Comput., 11, 4135–4151.
5.
BrooijmansN., and KuntzI.D.2003. Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 32, 335–373.
6.
CaoT.C., and LiT.H.2004. A combination of numeric genetic algorithm and tabu search can be applied to molecular docking. Comput. Biol. Chem., 28, 303–312.
7.
ChenH.M., LiuB.F., HwangS.F., et al.2007. SODOCK: Swarm optimization for highly flexible protein-ligand docking. J. Comput. Chem., 28, 612–623.
8.
FuhrmannJ., RurainskiA., LenhofH.P., et al.2010. A new Lamarckian genetic algorithm for flexible ligand-receptor docking. J. Comput. Chem., 31, 1911–1918.
9.
GoodsellD.S., and OlsonA.J.1990. Automated docking of substrates to proteins by simulated annealing. Proteins Struct. Funct. Genet., 8, 195–202.
10.
GuoL.Y., YanZ.Q., ZhengX.L., et al.2014. A comparison of various optimization algorithms of protein-ligand docking programs by fitness accuracy. J. Mol. Model., 20, 2251.
11.
HuX., BalazS., and ShelverW.H.2004. A practical approach to docking of zinc metalloproteinase inhibitors. J. Mol. Graph. Model., 22, 293–307.
12.
HuangS.Y., and ZouX.Q.2010. Advances and challenges in protein-ligand docking. Int. J. Mol. Sci., 11, 3016–3034.
13.
JasonS., MerkleD., and MiddendorfM.2008. Molecular docking with multi-objective particle swarm optimization. Appl. Soft. Comput., 8, 666–675.
14.
JonesG., WillettP., GlenR.C., et al.1997. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol., 267, 727–748.
15.
JugG., AnderluhM., and TomašičT.2015. Comparative evaluation of several docking tools for docking small molecule ligands to DC-SIGN. J. Mol. Model., 21, 164–178.
16.
KitchenD.B., DecomezH., FurrJ.R., et al.2004. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 3, 935–949.
17.
LiZ., GuJ., ZhuangH., et al.2015. Adaptive molecular docking method based on information entropy genetic algorithm. Appl. Soft. Comput., 26, 299–302.
18.
López-CamachoE., GodoyM.J., Garcia-NietoJ., et al.2015. Solving molecular flexible docking problems with metaheuristics: A comparative study. Appl. Soft Comput., 28, 379–393.
19.
MoitessierN., EnglebienneP., LeeD., et al.2008. Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br. J. Pharmacol., 153, 7–26.
20.
MorrisG.M., GoodsellD.S., HallidayR.S., et al.1998. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 19, 1639–1662.
21.
MorrisG.M., GoodsellD.S., HueyR., et al.1996. Distributed automated docking of flexible ligands to proteins: Parallel application of AutoDock 2.4. J. Comput. Aid. Mol. Des., 10, 293–304.
22.
MorrisG.M., HueyR., LindstromW., et al.2009. AutoDock4 and AutoDockTools 4: Automated docking with selective receptor flexibility. J. Comput. Chem., 30, 2785–2791.
23.
ThomsenR.2003. Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. Biosystems, 72, 57–73.
