Abstract
Knowledge of how small molecules interact with target proteins is of great interest in many fields, especially in drug discovery. 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. There are many available docking software and among them Autodock Vina is one of the most accurate and largely applied in many studies. 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. 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. 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. 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 results suggest that there is no difference whether several simulations with small Exhaustiveness or a few simulations with high Exhaustiveness setting are performed. In addition, the results give a good indication of the number of simulations and Exhaustiveness setting required for meaningful docking results with AutoDock Vina.
This study was supported by CAPES Edital Biologia Computacional (51/2013), CAPES Financial Code 001 and CNPq (439582/2018-0).
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Cecotti, L.K.S., Balboni, M.D.C., Arce, O.E.A., Machado, K.d.S., Werhli, A.V. (2022). A Non Exhaustive Search of Exhaustiveness. In: Scherer, N.M., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2022. Lecture Notes in Computer Science(), vol 13523. Springer, Cham. https://doi.org/10.1007/978-3-031-21175-1_11
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