sc-PDB: a database for identifying variations and multiplicity of ‘druggable’ binding sites in proteins

Meslamani, Jamel; Rognan, Didier; Kellenberger, Esther

doi:10.1093/bioinformatics/btr120

Cited by 98 publications

(108 citation statements)

References 9 publications

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“…The sc-PDB database has for example been expanded for identifying the variations and multiplicity of the druggable binding sites using a hierarchical clustering based on 8166 protein-ligand complexes [176]. The underlying rational is that a classification by binding site diversity avoids comparing ligands that do not share the same binding site and allows to quantitatively evaluate the diversity of the binding site definition (size, sequence, or structure).…”

Section: Current Trends In Ivsmentioning

confidence: 99%

Virtual screening: An in silico tool for interlacing the chemical universe with the proteome

Westermaier

Barril

Scapozza

2015

Methods

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Section: Current Trends In Ivsmentioning

confidence: 99%

Virtual screening: An in silico tool for interlacing the chemical universe with the proteome

Westermaier

Barril

Scapozza

2015

Methods

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“…41 The 2014 release of the Refined PDBbind data set contains a total of 3446 structures. This data set comprises only crystal structures with overall resolution below or equal to 2.5 Å, with no covalently bound ligands and in a binary complex.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

“…To further validate IsoMIF, we introduce two data sets derived from the PDBbind 49 2014 release and sc-PDB. 41 For PDBbind, only the 3446 entries in the refined set were considered. Within those 3446 entries, 1415 (41%) had at least one true positive entry within the set, that is, at least one other example bound to the same ligand.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Detection of Binding Site Molecular Interaction Field Similarities

Chartier

Najmanovich

2015

J. Chem. Inf. Model.

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Protein binding-site similarity detection methods can be used to predict protein function and understand molecular recognition, as a tool in drug design for drug repurposing and polypharmacology, and for the prediction of the molecular determinants of drug toxicity. Here, we present IsoMIF, a method able to identify binding site molecular interaction field similarities across protein families. IsoMIF utilizes six chemical probes and the detection of subgraph isomorphisms to identify geometrically and chemically equivalent sections of protein cavity pairs. The method is validated using six distinct data sets, four of those previously used in the validation of other methods. The mean area under the receiver operator curve (AUC) obtained across data sets for IsoMIF is higher than those of other methods. Furthermore, while IsoMIF obtains consistently high AUC values across data sets, other methods perform more erratically across data sets. IsoMIF can be used to predict function from structure, to detect potential cross-reactivity or polypharmacology targets, and to help suggest bioisosteric replacements to known binding molecules. Given that IsoMIF detects spatial patterns of molecular interaction field similarities, its predictions are directly related to pharmacophores and may be readily translated into modeling decisions in structure-based drug design. IsoMIF may in principle detect similar binding sites with distinct amino acid arrangements that lead to equivalent interactions within the cavity. The source code to calculate and visualize MIFs and MIF similarities are freely available.

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“…The Pocketome initiative is complementary to the binding affinity-centered databases such as PDBbind 7, 8 , Binding MOAD 9 , BindingDB 10 , AutoBind 11 , and shares some similar features with PDBSite 12 , ReliBase 13, 14 , MSDsite 15 , sc-PDB 16 , and LigBase 17 . The unique features of the Pocketome include:

Focus on the binding site; multiple binding sites on a single protein or domain are treated separately.

Complete definition of the binding site composition, including protein chains in a homo- or hetero-multimer, catalytic or structural metal ions, and cofactors binding concurrently with the ligands.

Ensemble nature, capturing the compositional and conformational variability of the pocket.

…”

Section: The Pocketome Encyclopediamentioning

confidence: 99%

Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

Kufareva¹,

Chen²,

Ilatovskiy³

et al. 2012

CTMC

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Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocket-based and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

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sc-PDB: a database for identifying variations and multiplicity of ‘druggable’ binding sites in proteins

Abstract: The sc-PDB database is freely available at: http://bioinfo-pharma.u-strasbg.fr/scPDB.

Cited by 98 publications

References 9 publications

Virtual screening: An in silico tool for interlacing the chemical universe with the proteome

Virtual screening: An in silico tool for interlacing the chemical universe with the proteome

Detection of Binding Site Molecular Interaction Field Similarities

Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

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