![]() Įberhardt, J., Santos-Martins, D., Tillack, A.F., Forli, S.: AutoDock vina 12 0: new docking methods, expanded force field, and python bindings. 20(1), 1–15 (2019)ĭhanik, A., McMurray, J.S., Kavraki, L.E.: DINC: a new AutoDock-based protocol for docking large ligands. 36(2), 78–95 (2015)ĭevaurs, D., et al.: Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. Accessed 18 July 2022Ĭhen, Y.C.: Beware of docking! Trends Pharmacol. In addition, the results give a good indication of the number of simulations and Exhaustiveness setting required for meaningful docking results with AutoDock Vina. The results suggest that there is no difference whether several simulations with small Exhaustiveness or a few simulations with high Exhaustiveness setting are performed. The main purpose of the analysis discussed in this paper is to help users define the Exhaustiveness parameter and thus achieve a good trade-off between simulation time and pose prediction quality. The quality of the Autodock Vina results is assessed by the distance between the experimental and the predicted ligand poses and by the Free Energy of Binding calculated by Autodock Vina. In this work, we investigate the Exhaustiveness parameter in a set of 4,463 protein-ligand complexes (PDBbind2018 refined dataset) for which the correct ligand pose is known. ![]() In Autodock Vina, among the different parameter settings, the Exhaustiveness is of crucial importance as it is directly related to the accuracy of the resulting poses. There are many available docking software and among them Autodock Vina is one of the most accurate and largely applied in many studies. ![]() Generally, this knowledge is obtained in time-consuming and very expensive wet experiments, which emphasises the importance of in silico computational predictions by docking simulations. Knowledge of how small molecules interact with target proteins is of great interest in many fields, especially in drug discovery. ![]()
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