Abstract
The AutoDock is a widely used protein-ligand docking simulation platform which is a simulator to bring the field of computer-aided drug design (CADD) conveniences. The protein-ligand docking problem is of great significance to design more effective and ideal drugs. In order to solve the protein-ligand docking problem more effectively, we propose an improved fireworks algorithm called FWADOCK. FWADOCK utilizes the position updating information of fireworks with the best fitness value to determine the shape of the fireworks and the distribution of sparks. During the process of searching for the optimal solution, it is also considered that the diversity of understanding and how to avoid premature convergence of the algorithm. The proposed algorithm is tested on some benchmark test cases of protein-ligand docking problems and compared with some other related algorithms from different perspectives on the AutoDock platform. The results show that FWADOCK has a competitive performance in solving the protein-ligand docking problem.
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References
Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32(1):335–373
Güner O (2000) Pharmacophore perception, development, and use in drug design. Int Univ Line 5(7):987–989
Huang SY, Zou X (2010) Advances and challenges in protein-ligand docking. Int J Mol Sci 11(8):3016–3034
Jug G, Anderluh M, Tomašič T (2015) Comparative evaluation of several docking tools for docking small molecule ligands to DC-SIGN. J Mol Model 21(6):1–12
Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil CR (2008) Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go. Br J Pharmacol 153(SUPPL. 1):7–26
Verlinde CL, Hol WG (1994) Structure-based drug design: progress, results and challenges. Structure 2(7):577–587
Huey R, Morris GM, Olson AJ, Goodsell DS (2007) Software news and update a semiempirical free energy force field with charge-based desolvation. J Comput Chem 10:1145–1152
Feinstein WP, Brylinski M (2015) Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets. Aust J Chem 7(1):18
Zeng X, Liao Y, Liu Y, Zou Q (2017) Prediction and validation of disease genes using HeteSim scores. IEEE/ACM Trans Comput Biol Bioinformatics (TCBB) 14(3):687–695
Jones G, Willett P, Glen R, Leach A, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748
Miller MD, Kearsley SK, Underwood DJ, Sheridan RE (1994) FLOG: a system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des 8:153–174
Rarey M, Kramer B, Lengauer T, Klebe G (1996) Predicting receptor-ligand interactions by an incremental construction algorithm. J Mol Biol 261(3):470–489
Li J, Zheng S, Tan Y (2014) Adaptive fireworks algorithm. IEEE Congr Evol Comput (CEC) 2014:3214–3221
Hu X, Balaz S, Shelver WH (2004) A practical approach to docking of zinc metalloproteinase inhibitors. J Mol Graph Model 22(4):293–307
Morris GM, Goodsell DS, Huey R, Olson AJ (1996) Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J Comput Aided Mol Des 10:293–304
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Softw News Updates 30(16):2786–2791
Jiang D, Huo L, Song H (2018) Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans Netw Sci Eng 1(1):1–12
Jiang D, Huo L, Lv Z et al (2018) A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans Intell Transp Syst 99:1–15
Jiang D, Wang Y, Lv Z et al (2019) Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans Ind Inf, online available. https://doi.org/10.1109/TII.2019.2930226
Jiang D, Wang W, Shi L et al (2018) A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans Netw Sci Eng 5(3):1–12
Jiang D, Huo L, Li Y (2018) Fine-granularity inference and estimations to network traffic for SDN. PLoS One 13(5):1–23
Wang F, Jiang D, Wen H et al (2019) Adaboost-based security level classification of mobile intelligent terminals. J Supercomput 75:1–19 Online available
Huo L, Jiang D, Zhu X, et al (2019) An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int J Commun Syst online available, pp 1–12
Wang F, Jiang D, Qi S (2019) An adaptive routing algorithm for integrated information networks. China Commun 7(1):196–207
Huo L, Jiang D (2019) Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommun Syst 23(4):1–11
Huo L, Jiang D, Lv Z (2018) Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks. Comput Electr Eng 66(2):316–331
Zhu J, Song Y, Jiang D et al (2018) A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of things. IEEE Internet Things J 5(4):2375–2385
Tang W, Zhang K, Jiang D (2018) Physarum-inspired routing protocol for energy harvesting wireless sensor networks. Telecommun Syst 67(4):745–762
Zhang B, Zheng Y, Zhang M, Chen S (2017) Fireworks algorithm with enhanced fireworks interaction. IEEE/ACM Trans Comput Biol Bioinformatics (TCBB) 14(1):42–55
Wang GG, Deb SS, Cui Z (2015) Monarch butterfly optimization. Neural Comput Applic 31:1–20 Springer, London
Cao T, Li T (2015) A combination of numeric genetic algorithm and Tabu search can be applied to molecular docking. Comput Biol Chem 28(4):303–312
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Comput Chem J 19(28):1639–1662
Guan B, Zhang C, Ning J (2016) EDGA: a population evolution direction-guided genetic algorithm for protein–ligand docking. J Comput Biol 23(7):585–596
Fuhrmann JAN, Rurainski A, Lenhof H, Neumann D (2010) A new Lamarckian genetic algorithm for flexible ligand-receptor docking. J Comput Chem 31(9):1911–1918
Guan B, Zhang C, Ning J (2017) Genetic algorithm with a crossover elitist preservation mechanism for protein–ligand docking. AMB Express 7(1):174
Chen H, Liu B, Huang H, Hwang S, Ho S (2006) SODOCK: swarm optimization for highly flexible protein – ligand docking. J Comput Chem 28(2):612–623
Guan B, Zhang C, Zhao Y (2018) An efficient ABC_DE_based hybrid algorithm for protein–ligand docking. Int J Mol Sci 19(4):1181
Huey R, Morris GM, Olson AJ, Goodsell DS (2007) A semiempirical free energy force field with charge-based desolvation. Softw News Update 28(6):1145–1152
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This work was funded by the National Natural Science Foundation Program of China (61572116 and 61572117). Thanks for the China Scholarship Council and reviewers.
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Liu, Z., Jiang, D., Zhang, C. et al. A Novel Fireworks Algorithm for the Protein-Ligand Docking on the AutoDock. Mobile Netw Appl 26, 657–668 (2021). https://doi.org/10.1007/s11036-019-01412-6
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DOI: https://doi.org/10.1007/s11036-019-01412-6