Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile

Réau, Manon; Lagarde, Nathalie; Zagury, Jean‐François; Montès, Matthieu

doi:10.1021/acs.jmedchem.8b01105

Cited by 18 publications

(13 citation statements)

References 54 publications

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“…Small molecules and protein structures were selected from NR-DBIND () [14]. This database provides affinity data for small molecules that were experimentally tested against NRs (including negative results) together with pharmacological profile annotations (whenever the information was available) and annotated protein structures.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we focus on the prediction of AR agonist compounds using virtual screening methods. The availability of experimentally validated AR-agonist/antagonist and non-binding compounds recorded in the Nuclear Receptor DataBase Including Negative Data (NR-DBIND) [14], and of numerous experimentally elucidated AR structures in an agonist-bound conformation from the Protein Data Bank (PDB), make both LB and SB methods relevant. Therefore, we compared the ability of a pharmacophore modeling (LB) approach and a docking (SB) approach to predict AR agonist compounds, i.e., the ability to discriminate the agonist compounds from antagonist and non-binding compounds.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

Réau

Lagarde

Zagury

et al. 2019

Cells

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The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

Réau

Lagarde

Zagury

et al. 2019

Cells

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“…We created a training set by combining the CryptoSite [13] and PDBbind [18,19] databases and used NR-DBIND [27] as the external validation set. The volume change between the holo and apo pockets was used as a metric to identify whether the ligand was an inducer.…”

Section: Discussionmentioning

confidence: 99%

“…We implemented the same workflow as the PDBbind dataset to build a dataset comprising apo and holo NR pairs. To this end, the nuclear receptor database (NR-DBIND) [27] was used as the starting point. These data indicated whether the PDB ID corresponded to a holo or apo structure, which helped us to validate the in-house-developed search script.…”

Section: Dataset Ii: Nr-dbindmentioning

confidence: 99%

What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?

et al. 2022

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Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo–apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins.

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“…During structure-based drug discovery, the three-dimensional structure of a therapeutic target protein is often determined redundantly in the form of a complex with various drug candidates, which reveals their binding modes at the atomic level [1][2][3][4]. Consequently, an abundance of structural information has been accumulated for some target proteins in Protein Data Bank (PDB) and other databases [5][6][7][8][9][10]. Ligand interactions can alter the structure of a receptor protein in different ways to produce structural variation in the protein [11,12] When examining protein structures, it becomes clear that ligand binding is not the sole cause of structural variation in crystals: crystal packing and sequence alteration also affect structure [13,14].…”

Section: Introductionmentioning

confidence: 99%

Allosteric regulation of 3CL protease of SARS-CoV-2 and SARS-CoV observed in the crystal structure ensemble

Kidera

Moritsugu

Ekimoto

et al. 2021

Preprint

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The 3C-like protease (3CLpro) of SARS-CoV-2 is a potential therapeutic target for COVID-19. Importantly, it has an abundance of structural information solved as a complex with various drug candidate compounds. Collecting these crystal structures (83 Protein Data Bank (PDB) entries) together with those of the highly homologous 3CLpro of SARS-CoV (101 PDB entries), we constructed the crystal structure ensemble of 3CLpro to analyze the dynamic regulation of its catalytic function. The structural dynamics of the 3CLpro dimer observed in the ensemble were characterized by the motions of four separate loops (the C-loop, E-loop, H-loop, and Linker) and the C-terminal domain III on the rigid core of the chymotrypsin fold. Among the four moving loops, the C-loop (also known as the oxyanion binding loop) causes the order (active)-disorder (collapsed) transition, which is regulated cooperatively by five hydrogen bonds made with the surrounding residues. The C-loop, E-loop, and Linker constitute the major ligand binding sites, which consist of a limited variety of binding residues including the substrate binding subsites. Ligand binding causes a ligand size dependent conformational change to the E-loop and Linker, which further stabilize the C-loop via the hydrogen bond between the C-loop and E-loop. The T285A mutation from SARS-CoV 3CLpro to SARS-CoV-2 3CLpro significantly closes the interface of the domain III dimer and allosterically stabilizes the active conformation of the C-loop via hydrogen bonds with Ser1 and Gly2; thus, SARS-CoV-2 3CLpro seems to have increased activity relative to that of SARS-CoV 3CLpro.

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Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile

Cited by 18 publications

References 54 publications

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?

Allosteric regulation of 3CL protease of SARS-CoV-2 and SARS-CoV observed in the crystal structure ensemble

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