Novel Scaffold Identification of <scp>mG</scp>lu1 Receptor Negative Allosteric Modulators Using a Hierarchical Virtual Screening Approach

Jang, Jae Wan; Cho, Nam Chul; Min, Sun‐Joon; Cho, Yong Seo; Park, Ki Duk; Seo, Seon Hee; No, Kyoung Tai; Pae, Ae Nim

doi:10.1111/cbdd.12654

Cited by 22 publications

(27 citation statements)

References 89 publications

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“…Virtual screening can be also used to identify allosteric modulators of GPCRs as it was done for muscarinic receptors [ 112 ] and metabotropic glutamate receptors [ 113 ]. Other successful examples of structure-based discovery of GPCR allosteric ligand include identification of GPR68 modulators by Huang et al [ 114 ], and muscarinic M 2 receptor modulators by Miao et al [ 112 ].…”

Section: High-throughput Docking As a Methods Of Virtual Screeningmentioning

confidence: 99%

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

et al. 2017

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The growing number of studies on G protein-coupled receptors (GPCRs) family are a source of noticeable improvement in our understanding of the functioning of these proteins. GPCRs are responsible for a vast part of signaling in vertebrates and, as such, invariably remain in the spotlight of medicinal chemistry. A deeper insight into the underlying mechanisms of interesting phenomena observed in GPCRs, such as biased signaling or allosteric modulation, can be gained with experimental and computational studies. The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods. One of the key steps in such studies is proper computational reconstruction of actual ligand-receptor or protein-protein interactions, a process called molecular docking. A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.

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Section: High-throughput Docking As a Methods Of Virtual Screeningmentioning

confidence: 99%

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

et al. 2017

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“…It has been shown that it is possible for homology models of GPCRs to identify potential ligands . There have been a number of studies aiming to evaluate the performance of homology models of GPCRs for in‐silico drug screening . For example, Tang et al studied beta‐2 adrenoreceptor (ADRB2) and showed that some homology models could exceed crystal structures in ligand virtual screening .…”

Section: Introductionmentioning

confidence: 99%

“…14 There have been a number of studies aiming to evaluate the performance of homology models of GPCRs for in-silico drug screening. [14][15][16][17][18][19][20][21] For example, Tang et al studied beta-2 adrenoreceptor (ADRB2) and showed that some homology models could exceed crystal structures in ligand virtual screening. 22 However, all of them only focus on 1 or a few GPCRs and their homology models, which do not provide a complete picture about the capability of homology model based methods for virtual screening of GPCR ligands.…”

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confidence: 99%

A benchmarking study on virtual ligand screening against homology models of human GPCRs

Lim

Chen

et al. 2018

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G-protein-coupled receptor (GPCR) is an important target class of proteins for drug discovery, with over 27% of FDA-approved drugs targeting GPCRs. However, being a membrane protein, it is difficult to obtain the 3D crystal structures of GPCRs for virtual screening of ligands by molecular docking. Thus, we evaluated the virtual screening performance of homology models of human GPCRs with respect to the corresponding crystal structures. Among the 19 GPCRs involved in this study, we observed that 10 GPCRs have homology models that have better or comparable performance with respect to the corresponding X-ray structures, making homology models a viable choice for virtual screening. For a small subset of GPCRs, we also explored how certain methods like consensus enrichment and sidechain perturbation affect the utility of homology models in virtual screening, as well as the selectivity between agonists and antagonists. Most notably, consensus enrichment across multiple homology models often yields results comparable to the best performing model, suggesting that ligand candidates predicted with consensus scores from multiple models can be the optimal option in practical applications where the performance of each model cannot be estimated.

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“…It has been shown that it is possible for homology models of GPCRs to identify potential ligands 14 . There have been a number of studies aiming to evaluate the performance of homology models of GPCRs for in-silico drug screening [14][15][16][17][18][19][20][21] . For example, Tang et al studied beta-2 adrenoreceptor (ADRB2) and showed that some homology models could exceed crystal structures in ligand virtual screening 22 .…”

Section: Introductionmentioning

confidence: 99%

A benchmarking study on virtual ligand screening against homology models of human GPCRs

Lim

Chen

et al. 2018

Preprint

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G-protein-coupled receptor (GPCR) is an important target class of proteins for drug discovery, with over 27% of FDA-approved drugs targeting GPCRs. However, being a membrane protein, it is difficult to obtain the 3D crystal structures of GPCRs for virtual screening of ligands by molecular docking. Thus, we evaluated the virtual screening performance of homology models of human GPCRs with respect to the corresponding crystal structures. Among the 19 GPCRs involved in this study, we observed that 10GPCRs have homology models that have better or comparable performance with respect to the corresponding X-ray structures, making homology models a viable choice for virtual screening. For a small subset of GPCRs, we also explored how certain methods like consensus enrichment and sidechain perturbation affect the utility of homology models in virtual screening, as well as the selectivity between agonists and antagonists. Most notably, consensus enrichment across multiple homology models often yields results comparable to the best performing model, suggesting that ligand candidates predicted with consensus scores from multiple models can be the optimal option in practical applications where the performance of each model cannot be estimated.

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Novel Scaffold Identification of mGlu1 Receptor Negative Allosteric Modulators Using a Hierarchical Virtual Screening Approach

Cited by 22 publications

References 89 publications

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

A benchmarking study on virtual ligand screening against homology models of human GPCRs

A benchmarking study on virtual ligand screening against homology models of human GPCRs

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