The protein–small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding

Wallach, Izhar; Lilien, Ryan H.

doi:10.1093/bioinformatics/btp035

Cited by 55 publications

(33 citation statements)

References 28 publications

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“…PDB-bench was compiled from the Protein Small Molecule Database [50] and the Protein Data Bank (PDB) [51] by including only proteins 50–600 residues in length with the redundancy removed at 40 % pairwise sequence identity using PISCES [52]. The length constraints are imposed due to the subsequent use of protein threading, however, these do not exclude pharmacologically relevant molecules such as G-protein coupled receptors (GPCRs) and protein kinases.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

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

2015

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“…The representative pockets were selected from the Protein-Small-Molecule DataBase (PSMDB) [67]. Among several non-redundant datasets of structures of protein-ligand complexes provided in PSMDB, we chose the list available at http://compbio.cs.toronto.edu/psmdb/downloads/CPLX_25_0.85_7HA.list, where proteins were pruned with 25% sequence identity and redundant ligands that have a Tanimoto coefficient of 0.85 or higher to other ligand molecules were filtered out.…”

Section: Patch-surfer Results On the Representative Binding Pocket Damentioning

confidence: 99%

Structure- and sequence-based function prediction for non-homologous proteins

Sael

Chitale

2012

J Struct Funct Genomics

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The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recently developments of local structure-based methods and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, PFP and ESG, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.

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“…This database was constructed from protein-small-molecule database (PSMDB) [67,68]. Patch-Surfer is available as a webserver at http://kiharalab.org/patchsurfer2.0/.…”

Section: Methodsmentioning

confidence: 99%

PatchSurfers: Two methods for local molecular property-based binding ligand prediction

Shin

Bures

2016

Methods

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Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins’ biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: 1) rotational invariance and 2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided.

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The protein–small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding

Abstract: http://compbio.cs.toronto.edu/psmdb

Cited by 55 publications

References 28 publications

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

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

Structure- and sequence-based function prediction for non-homologous proteins

PatchSurfers: Two methods for local molecular property-based binding ligand prediction

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