AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, theydescribe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, they implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs.

AutoDock Vina (Vina) is one of the docking programs in the AutoDock Suite, together with AutoDock4 (AD4), AutoDock-GPU, AutoDockFR, and AutoDock-CrankPep. Vina is arguably among the most widely used programs, probably because of its ease of use and speed, when compared to the other docking programs in the suite and elsewhere, as well as being open source.

Research groups around the world have modified and built upon the Vina source code, improving the search algorithm made the interface more user friendly allowing modification of scoring terms through the user interface , and improved the scoring function for carbohydrate docking and halogen bonds, as well as ranking and scoring.

Besides these valuable developments, there are still several methods within the AutoDock Suite that are not available in the Vina program because they have been implemented specifically for either the AD4 scoring function or the AD4 program. Examples of such methods include docking with macrocyclic flexibility, specialized metal coordination models, modeling of explicit water molecules, coarse-grained ligand models, and ligand-irreversible binding. Despite being a less-efficient program, AD4 allows the user to modify a large number of docking parameters, providing direct access to some of the engine internals, making it well suited for the development of new docking methods. Conversely, the Vina interface is highly specialized and optimized, and one of its hallmarks is the very limited amount of user input necessary to perform a docking. In turn, this makes it impossible to implement additional functionality without significant changes in the source code.

The usefulness of such specialized methods is hindered by the poor search efficiency of the AD4 program. In fact, AD4 can be up to 100× slower than Vina, depending on the search complexity. The large performance difference is due to the better search algorithm used in Vina, a Monte-Carlo (MC) iterated search combined with the BFGS gradient-based optimizer. In comparison with the Lamarckian genetic algorithm and Solis-Wets local search of AD4, the search efficiency of Vina leads to better docking results with fewer scoring function evaluations.

Here, they implemented the AD4 scoring function in the Vina program. Furthermore, some of the specialized features available in AD4 were also ported to the Vina source code, enabling their use with the Vina’s powerful MC/BFGS search algorithm. Then, they further extended the Vina program enabling simultaneous docking of multiple ligands, and added Python bindings to facilitate programmatic access to the docking engine functionalities.

Example applications of AutoDockVina 1.2.0 for docking (A) multiple ligands, (B) with water molecules using the hydrated docking protocol from AutoDock4 , (C) in the presence of zinc using the AutoDock4Zn forcefield , or (D) flexible macrocycles (compound 19 from the BACE dataset of the D3R Grand Challenge 4). Proteins are represented in white cartoon and crystal poses and protein residues in white thin sticks. The 2Fo–Fc electron-density map, contoured at 2.0σ, is colored gray. The docking poses are represented in sticks, and colored in green and orange when docked using the Vina or AutoDock4 scoring function, respectively. Docking with zinc was done in the presence of the farnesyl disphosphate molecule, represented in sticks and colored in white.

Docking success rate of six ligands redocked against HSP90, using the AutoDock4.2 scoring function, with and without the hydrated docking protocol considering the top 1, top 2, and top 3 poses. The pose prediction was considered as successful if the RMSD was inferior than 2, 1, or 0.5 Å from the crystal pose.

Early recognition of active compounds from the DUD-E dataset and crystal pose prediction. All 102 targets from the DUD-E dataset were selected and used to compare Vina and AutoDock4.2 scoring functions in AutoDock Vina. Violin plots of (A) AUC, (B) BEDROC using an α of 160.9 and (C) EF at 1%. (D) Docking success rate for Vina and AutoDock4.2 scoring functions using crystal poses considering the top 1, top 2, and top 3 poses. The pose prediction was considered as successful if the RMSD was inferior than 2, 1, or 0.5 Å from the crystal pose.

  1. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings Jerome Eberhardt, Diogo Santos-Martins, Andreas F. Tillack, and Stefano Forli Journal of Chemical Information and Modeling 2021 61 (8), 3891-3898 DOI: 10.1021/acs.jcim.1c00203