You can either download a zip file or an installer of MGLTools. Here, we will install using the Windows installer. Download it from here. It can run on both bit and bit supporting architecture. Double click on the download file. It will ask to accept the agreement followed by the location to install MGLTools.
Accept the agreement and select an appropriate drive and folder to install. Double click on the downloaded msi file. Select an appropriate location to install and wait to finish. There you will find vina. Now, you can see shortcuts would have been created on your desktop. Double click Autodock-Tools shortcut to start graphical user interface. There you can prepare your receptor and ligands for docking.
For this, you can refer to blind docking and site-specific docking articles. After you prepare all files, keep them in a same folder. Open a command prompt, provide the full path to vina executable vina. The procedure and command to run Vina on Windows are explained in this article.
Like in AutoDock 4, some receptor side chains can be chosen to be treated as flexible during docking. AutoDock Vina tends to be faster than AutoDock 4 by orders of magnitude. Some of these projects average over 50 years worth of computation per day. Average time per receptor-ligand pair on the test set. License AutoDock Vina is released under a very permissive Apache license, with few restrictions on commercial or non-commercial use, or on the derivative works.
The text of the license can be found here. Trott, A. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, Journal of Computational Chemistry 31 Features Accuracy AutoDock Vina significantly improves the average accuracy of the binding mode predictions compared to AutoDock 4, judging by our tests on the training set used in AutoDock 4 development.
The feasibility of a VS experiment on a given infrastructure can be measured in terms of how long the experiment takes. The longer the calculation time, the less feasible an experiment can become due to practical reasons.
Especially, time is directly proportional to cost when performing VS on pay-per-use infrastructures. The aim of this paper is to provide a concise review together with experimental analysis of the impact of variations in the VS experiment setup and the types of HPC infrastructures on the execution time.
This enables a well-informed decision by biochemists when setting up their experiments on HPC platforms. In the experiment set-up, we specifically look at the properties of individual ligands as well as Vina configuration parameters. We use libraries of known and available drugs that are common in biomedical studies.
Also, various methods have been developed to speed up their execution [ 8 , 9 , 10 , 11 , 12 ]. We take AutoDock Vina as a typical, arguably most popular, molecular docking tool available for virtual screening. Popularity is explained by being free and the quality of the results, especially for ligands with 8 or more rotatable bonds [ 13 ].
Although this paper is based on AutoDock Vina, the findings reported here possibly also apply to similar software packages and analogous types of experiments. For brevity, we will refer to AutoDock Vina simply as Vina in the rest of the paper. Paper structure In the rest of this section, we introduce the basic concepts and features of Vina required for setting up a VS experiment. We then explain the characteristics of the four types of computing infrastructures used to run our experiments.
The Methods section presents the ligands and proteins used in the experiments, their various set-ups and configurations, as well as the details of how we ran the experiments on each infrastructure.
AutoDock Vina [ 3 ] is a well-known tool for protein-ligand docking built in the same research lab as the popular tool AutoDock 4 [ 14 , 15 ]. It implements an efficient optimization algorithm based on a new scoring function for estimating protein-ligand affinity and a new search algorithm for predicting the plausible binding modes. Additionally, it can run calculations in parallel using multiple cores on one machine in order to speed up the computation. In this paper, we adopt the following terminology the italicized terms.
One execution of Vina tries to predict where and how a putative ligand can best bind to a given protein, in which Vina may repeat the calculations several times with different randomizations the configuration parameter exhaustiveness controls how many times to repeat the calculations. The part of the protein surface where the tool attempts the binding is specified by the coordinates of a cuboid, to which we refer as the docking box.
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