A Non Exhaustive Search of Exhaustiveness - Cecotti, Letícia Kristian Silva; Balboni, Maurício Dorneles Caldeira; Arce, Oscar Emilio Arrúa; Machado, Karina dos Santos; Werhli, Adriano Velasque (Anais do Simpósio Brasileiro de Bioinformática (BSB); 2022: Anais do XV Simpósio Brasileiro de Bioinformática, 2022)
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.