A Critical Assessment of Docking Programs and Scoring Functions

Warren, Gregory L.; Andrews, C. Webster; Capelli, Anna‐Maria; Clarke, Brian; LaLonde, Judith M.; Lambert, Millard H.; Lindvall, Mika K.; Nevins, Neysa; Semus, Simon F.; Senger, Stefan; Tedesco, Giovanna; Wall, Ian D.; Woolven, James M.; Peishoff, Catherine E.; Head, Martha S.

doi:10.1021/jm050362n

Cited by 1,471 publications

(1,582 citation statements)

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“…The present assessment of the accuracy of AutoDock for protein-ligand docking concurs with earlier results, 15 in that, the predicted binding configurations are generally quite close to the target configurations but the binding energies are not very reliable. Thus AutoDock predictions for the binding positions can be profitably used as initial configurations in MD simulations, with the provision that the ligand does not come into contact with the peptide (cf.…”

Section: Resultssupporting

confidence: 85%

“…14 For example, a recent test of various docking programs revealed that none of them could reliably predict the ligand binding affinities. 15 On the positive side, docking predictions for the binding configurations were not too far from the experimentally determined bound complex structures, 15 giving hope that docking methods may provide useful starting configurations for MD simulations.…”

Section: Introductionmentioning

confidence: 99%

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Binding of Organic Cations to Gramicidin A Channel Studied with AutoDock and Molecular Dynamics Simulations

2007

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The accurate description of protein-ligand binding energies and configurations is an important problem in molecular biology with many applications in medicine and pharmacology. Molecular dynamics (MD) simulations provide the required accuracy but they are too slow for searching binding positions. Conversely, docking methods are much faster but have limited accuracy. An appropriate combination of the two methods could avoid the shortcomings associated with each, thus offering a better approach to the protein-ligand binding problem. Here we investigate the feasibility of such a combined docking-MD approach in a welldefined system: binding of organic cations to the gramicidin A channel. We use the AutoDock program to generate a set of protein-ligand binding configurations, which are then refined in MD simulations. For each system, we examine the binding configuration in detail and calculate the binding free energy by constructing the potential of mean force for the ligand. Our results show that AutoDock provides suitable initial configurations, which can be used profitably in MD simulations to obtain an accurate description of proteinligand binding with a reasonable computational effort.

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Section: Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Binding of Organic Cations to Gramicidin A Channel Studied with AutoDock and Molecular Dynamics Simulations

2007

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“…Some programs have been reported to be more suitable for some protein systems than other proteins. 1 Understanding the performance of LibDock for a particular protein system may provide information on how best to use the algorithm in the future.…”

Section: Resultsmentioning

confidence: 99%

“…The recently reported GSK docking-scoring study 1 included two programs, MVP 12 and Fred, 20 which used binding site information in their docking-scoring algorithms. The first program, MVP, used atom-type target points to superimpose ligands in the binding site and performed well in both docking accuracy and retrieval of actives in a virtual screening.…”

Section: Introductionmentioning

confidence: 99%

Validation Studies of the Site-Directed Docking Program LibDock

Rao

Head

Kulkarni

et al. 2007

J. Chem. Inf. Model.

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297

199

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The performance of the site-features docking algorithm LibDock has been evaluated across eight GlaxoSmithKline targets as a follow-up to a broad validation study of docking and scoring software (Warren, This study was conducted using DJD scoring, and poses were rescored using all available scoring functions in the Accelrys LigandFit module, including LigScore2. For six out of eight targets at least 30% of the ligands were docked within a root-mean-square difference (RMSD) of 2.0 Å for the crystallographic poses when the LigScore2 scoring function was used. LibDock retrieved at least 20% of active compounds in the top 10% of screened ligands for four of the eight targets in the virtual screening protocol. In both studies the LigScore2 scoring function enhanced the retrieval of crystallographic poses or active compounds in comparison with the results obtained using the DJD scoring function. The results for LibDock accuracy and ligand retrieval in virtual screening are compared to 10 other docking and scoring programs. These studies demonstrate the utility of the LigScore2 scoring function and that LibDock as a feature directed docking method performs as well as docking programs that use genetic/growing and Monte Carlo driven algorithms.G

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“…Much work needs to be done to improve the scoring functions towards a direct estimation of free energies of binding (see structure-based methodologies section). However, docking programs have demonstrated a substantial reliability in the prediction of ligand binding modes and bioactive conformations [83]. Dealing with crystal structures of nuclear hormone receptors and enzymes, Sippl amply demonstrated the advantages of combining the accuracy of ligand alignments obtained through docking with the computational efficiency of 3D-QSAR methodologies [84][85][86][87].…”

Section: Combining Ligand and Structure-based Methodsologiesmentioning

confidence: 99%

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Costanzi

Tikhonova

Harden

2008

J Comput Aided Mol Des

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SummaryAccurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

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A Critical Assessment of Docking Programs and Scoring Functions

Cited by 1,471 publications

References 50 publications

Binding of Organic Cations to Gramicidin A Channel Studied with AutoDock and Molecular Dynamics Simulations

Binding of Organic Cations to Gramicidin A Channel Studied with AutoDock and Molecular Dynamics Simulations

Validation Studies of the Site-Directed Docking Program LibDock

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

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