Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity

By Nguyen Thanh Nguyen, T. Nguyen, T. N. Pham, Nguyen Truong Huy, M. Bay, Minh Quan Pham, P. Nam, V. Vu, Son Tung Ngo. Journal of Chemical Information and Modeling, 2019

380 citations recorded by the source corpus.

The binding pose and affinity between a ligand to an enzyme are very important pieces of information for computer-aided drug design. In the initial stage of a drug discovery project, this information is often obtained by using molecular docking methods. Autodock4 and Autodock Vina are two commonly used open-source and free software to perform this task and each has been cited more than 6000 times in the last ten years. It is of great interest to compare the success rate of the two docking software for a large and diverse set of protein-ligand complexes. In this study, we selected 800 protein-ligands complexes for which both PDB structures and experimental binding affinity are available. Docking calculations were performed for these complexes using both Autodock4 and Autodock Vina with different docking options related to computing resource consumption and accuracy. Our calculation results are in good agreement with a previous study that the Vina approach converges much faster than AD4 one. However, interestingly, AD4 shows a better performance than Vina over 21 considered targets, whereas Vina protocol is better than AD4 package for 10 other targets. There are 16 complexes for which both AD4 and Vina protocols fail to produce a reasonable correlation with respected experiments so both are not suitable to use to estimate binding free energies for these cases. In addition, the best docking option for performing AD4 approach is the long option. However, the short option is the best solution for carrying out Vina docking. The obtained results probably will be useful for future docking studies in deciding which program to use.

Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cbdcb9bb0c3370d447363ec48dfbe278c0972a04