AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Garrett M. MorrisRuth HueyWilliam LindstromMichel F. SannerRichard K. BelewDavid S. GoodsellArthur J. Olson
20,412
Citation
18
Reference
10
Related Paper
Citation Trend
Abstract:
Abstract We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand‐protein complexes and a cross‐docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid‐based docking method and a modification of the flexible sidechain technique. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009Keywords:
Docking (animal)
Protein–ligand docking
Graphical user interface
We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.
Docking (animal)
Protein–ligand docking
Macromolecular docking
Cite
Citations (1)
Docking (animal)
Protein–ligand docking
Cite
Citations (414)
Treating flexibility in molecular docking is a major challenge in cell biology research. Here we describe the background and the principles of existing flexible protein-protein docking methods, focusing on the algorithms and their rational. We describe how protein flexibility is treated in different stages of the docking process: in the preprocessing stage, rigid and flexible parts are identified and their possible conformations are modeled. This preprocessing provides information for the subsequent docking and refinement stages. In the docking stage, an ensemble of pre-generated conformations or the identified rigid domains may be docked separately. In the refinement stage, small-scale movements of the backbone and side-chains are modeled and the binding orientation is improved by rigid-body adjustments. For clarity of presentation, we divide the different methods into categories. This should allow the reader to focus on the most suitable method for a particular docking problem.
Docking (animal)
Protein–ligand docking
Macromolecular docking
Ramachandran plot
Cite
Citations (215)
We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.
Docking (animal)
Protein–ligand docking
Macromolecular docking
Cite
Citations (1)
Small molecule docking predicts the interaction of a small molecule ligand with a protein at atomic-detail accuracy including position and conformation the ligand but also conformational changes of the protein upon ligand binding. While successful in the majority of cases, docking algorithms including RosettaLigand fail in some cases to predict the correct protein/ligand complex structure. In this study we show that simultaneous docking of explicit interface water molecules greatly improves Rosetta’s ability to distinguish correct from incorrect ligand poses. This result holds true for both protein-centric water docking wherein waters are located relative to the protein binding site and ligand-centric water docking wherein waters move with the ligand during docking. Protein-centric docking is used to model 99 HIV-1 protease/protease inhibitor structures. We find protease inhibitor placement improving at a ratio of 9∶1 when one critical interface water molecule is included in the docking simulation. Ligand-centric docking is applied to 341 structures from the CSAR benchmark of diverse protein/ligand complexes [1]. Across this diverse dataset we see up to 56% recovery of failed docking studies, when waters are included in the docking simulation.
Docking (animal)
Protein–ligand docking
Cite
Citations (69)
Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to
DOCK
Docking (animal)
Protein–ligand docking
Cite
Citations (9)
Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach
Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.
Docking (animal)
Protein–ligand docking
DOCK
Cite
Citations (50)
The presence of water molecules plays an important role in the accuracy of ligand−protein docking predictions. Comprehensive docking simulations have been performed on a large set of ligand−protein complexes whose crystal structures contain water molecules in their binding sites. Only those water molecules found in the immediate vicinity of both the ligand and the protein were considered. We have investigated whether prior optimization of the orientation of water molecules in either the presence or absence of the bound ligand has any effect on the accuracy of docking predictions. We have observed a statistically significant overall increase in accuracy when water molecules are included during docking simulations and have found this to be independent of the method of optimization of the orientation of water molecules. These results confirm the importance of including water molecules whenever possible in a ligand−protein docking simulation. Our findings also reveal that prior optimization of the orientation of water molecules, in the absence of any bound ligand, does not have a detrimental effect on the improved accuracy of ligand−protein docking. This is important, given the use of docking simulations to predict the binding modes of new ligands or drug molecules.
Docking (animal)
Protein–ligand docking
Cite
Citations (141)
We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.
Docking (animal)
Protein–ligand docking
Macromolecular docking
Cite
Citations (14)
Molecular docking is a computational screening approach in drug design able to predict the conformation of a protein-ligand complex. Docking algorithms provide an efficient and cost-effective alternative to experimental high-throughput screenings. Molecular docking is generally applied starting from the knowledge of the protein binding region. However, a precise information about the correct binding site is often missing and it becomes necessary to explore the entire protein surface by docking algorithms. Several methods have been developed to overcome the problem of not recognizing the binding site. In the present thesis, a new methodology to identify the experimental binding mode of small molecule ligands into protein structures where the real binding sites are unknown will be presented. The approach consists of to carry out ligand-protein docking separately in multiple fragmented boxes, shifting the location of the box step by step, in order to cover the entire surface of the protein. This fragmented docking has been compared with the blind docking performed by standard docking protocols on 116 protein-ligand complexes of Heat Shock Protein 90 – alpha and 177 of Human Immunodeficiency virus protease 1. The fragmented docking has demonstrated its ability to identify more accurate docking poses than blind docking performed by Autodock-Vina. In order to improve the docking results Molecular-Mechanics/Generalized-Born-Surface-Area has been employed to rescore the docking outcomes. The results deriving from this rescoring show that MM/GBSA is able further increase the accuracy of the approach. The method is relived a good compromise between accuracy and computational effort. Further challenge could be accomplished by calculating the affinity with more rigorous methods to improve the performance achieved.
Docking (animal)
Protein–ligand docking
AutoDock
Cite
Citations (0)