MOLECULAR DOCKING WITH AUTODOCK TOOLS
Computational docking is widely used for the study of protein-ligand interactions and for drug discovery and development. Typically, the process starts with a target of known structure, such as a crystallographic structure of an enzyme of medicinal interest. Docking is then used to predict the bound conformation and binding free energy of small molecules to the target. Single docking experiments are useful for exploring the function of the target, and virtual screening, where a large library of compounds are docked and ranked, may be used to identify new inhibitors for drug development.
AutoDock is a suite of free open–source software for the computational docking and virtual screening of small molecules to macromolecular receptors. The suite currently includes several complementary tools:
• AutoDock Vina: a turnkey computational docking program based on a simple scoring function and rapid gradient-optimization conformational search.
· AutoDock: a computational docking program based on an empirical free energy force field and rapid Lamarckian genetic algorithm search methods.
• Raccoon2: an interactive graphical tool for virtual screening and analysis.
• AutoDockTools: an interactive graphical tool for coordinate preparation, docking, and analysis.
• AutoLigand: a program for predicting optimal sites of ligand binding on receptors.
The AutoDock suite, including source, is freely available and has been widely used in research and drug discovery.
A variety of academic and commercial methods for computational ligand docking are currently available. Most of these methods simplify the problem in two ways to make the computation tractable.
First, the conformational space is reduced by imposing limitations to the system, such as a rigid receptor and fixed bond angles and lengths in the ligand. Second, a simplified scoring function, often based on empirical free energies of binding, is used to score poses quickly at each step of the conformation search.
Both of these are serious limitations, and users must employ tools such as molecular dynamics or free energy perturbation if a more realistic conformational search or energy prediction is necessary.
These tools are complementary with computational docking methods since docking methods generally search a larger conformational space, but more advanced methods can predict conformation and energy more accurately within a local area of the conformational landscape.
Advanced docking methods may be used to improve results in cases where the limitations of requiring a rapid method for energy evaluation are too restrictive. For instance, many docking methods employ a rigid model for the receptor, which often leads to improper results for proteins with appreciable induced fit upon binding.
AutoDock includes a method for treating a selection of receptor sidechains explicitly, to account for limited conformational changes in the receptor. In addition, ordered water molecules often mediate interactions between ligands and receptors, and advanced methods for treating selected waters explicitly have been implemented in AutoDock. Both of these advanced methods are demonstrated in this protocol.
Many reports have compared the performance of popular docking methods such as AutoDock. Different methods can achieve different success rates depending on specific targets, but in general, they all perform similarly when tested on a series of diverse protein-ligand complexes: they all perform well for the prediction of bound complexes for drug-sized molecules, with estimates of free energies of binding with errors of roughly 2–3 kcal/mol, provided that there is no significant motion required in the receptor. Better results may be obtained by tuning the docking method for a particular system or moving to more sophisticated and computationally-intensive parameterizations of the system.
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