Methods for the Prediction of Protein-Ligand Binding Sites for Structure-Based Drug Design and Virtual Ligand Screening.

Laurie, Alasdair T. R.; Jackson, Richard M.

doi:10.2174/138920306778559386

Cited by 152 publications

(33 citation statements)

References 49 publications

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“…A number of methods exist for binding site detection. These methods largely fall into one of three categories: (1) geometry-based methods, (2) knowledge-based methods, and (3) energy-based methods 55 . In geometry-based methods, cavity size is measured and the ligand binding site is identified as the largest pocket.…”

Section: Introductionmentioning

confidence: 99%

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins

et al. 2015

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FTMap is a computational mapping server that identifies binding hot spots of macromolecules, i.e., regions of the surface with major contributions to the ligand binding free energy. To use FTMap, users submit a protein, DNA, or RNA structure in PDB format. FTMap samples billions of positions of small organic molecules used as probes and scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots, in good agreement with experimental data. FTMap serves as basis for other servers, namely FTSite to predict ligand binding sites, FTFlex to account for side chain flexibility, FTMap/param to parameterize additional probes, and FTDyn to map ensembles of protein structures. Applications include determining druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures, and providing input for fragment based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and is much faster than the more recent approaches to protein mapping based on mixed molecular dynamics. Using 16 probe molecules, the FTMap server finds the hot spots of an average size protein in less than an hour. Since FTFlex performs mapping for all low energy conformers of side chains in the binding site, its completion time is proportionately longer.

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Section: Introductionmentioning

confidence: 99%

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins

et al. 2015

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“…For example, protein tertiary structures can be reliably modeled using amino acid sequences [1–3] to help infer their molecular functions [4–6]. Furthermore, putative ligand binding pockets can be confidently predicted from these computer-generated protein models [7–9] and used as target sites for the discovery of new pharmaceuticals [10–12]. Among various technologies developed to date, molecular docking has profound applications in drug design, e.g.…”

Section: Introductionmentioning

confidence: 99%

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

2015

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BackgroundComputational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated.MethodsIn this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite.ResultsA systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins.ConclusionsThis fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size.Graphical AbstractWe developed a procedure to optimize the box size in molecular docking calculations. Left panel shows the predicted binding pose of NADP (green sticks) compared to the experimental complex structure of human aldose reductase (blue sticks) using a default protocol. Right panel shows the docking accuracy using an optimized box size.

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“…Apart from the methods devoted to prediction of functional sites, alternative approaches such as molecular dynamics simulations [58] or docking [59–61] were successfully employed to identify ligand-binding sites. A thorough review of strategies for ligand-binding site detection is presented elsewhere [62]. …”

Section: Introductionmentioning

confidence: 99%

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

Prymula

Jadczyk

Roterman

2010

J Comput Aided Mol Des

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The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-010-9402-0) contains supplementary material, which is available to authorized users.

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Methods for the Prediction of Protein-Ligand Binding Sites for Structure-Based Drug Design and Virtual Ligand Screening.

Cited by 152 publications

References 49 publications

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

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