Multiple Fragment Docking and Linking in Primary and Secondary Pockets of Dopamine Receptors

Vass, Márton; Ágai-Csongor, Éva; Horti, Ferenc; Keserü, György M.

doi:10.1021/ml500201u

Cited by 31 publications

(28 citation statements)

References 16 publications

(25 reference statements)

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“…We got the docking score of 7.76 for salvinorin A within the DRD2 homology model. The homology model of DRD2 was established according to the template of DRD3 (66, 67). Figure 6D shows that Cys118, Ser194 and Ser197 formed strong hydrogen bonds with the ester moiety of salvinorin A (all hydrogen bonds are within 3 Å).…”

Section: Resultsmentioning

confidence: 99%

“…Salvinorin A fits well within the binding pocket formed by helix III, V, VI and VII. The important residues in the binding pocket are Asp114, Cys118, Ser194, Ser197, Trp342, Phe389, His393, Thr412 and Tyr416 (Figure 6C) (66, 67). Based on these similarities, we suggested salvinorin A might bind to DRD2, which also contribute to the pharmacological effects of salvinorin A (detailed explanations were in discussion session).…”

Section: Resultsmentioning

confidence: 99%

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Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification—Salvinorin A as a case study

Feng

et al. 2016

Journal of Molecular Graphics and Modelling

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Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification—Salvinorin A as a case study

Feng

et al. 2016

Journal of Molecular Graphics and Modelling

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“…In this regard, it is noteworthy that our approach is based on conventional cell-based screening, enabling us to identify partner ligands or a ligand pair without biophysical detection techniques that are typically used for fragment screening. Furthermore, recent development of a sequential docking methodology (35,36) may support our approach, in particular, for the characterization of the binding modes of multiple ligands and for the design of hybrid ligands. These tools and their potential applications will make our approach an even more effective strategy for creating novel PPARγ ligands.…”

Section: Hybrid Ligand 5 Is a Covalentmentioning

confidence: 99%

Identification of a New Type of Covalent PPARγ Agonist using a Ligand-Linking Strategy

Ohtera¹,

Miyamae²,

Yoshida³

et al. 2015

ACS Chem. Biol.

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Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor that plays an important role in adipogenesis and glucose metabolism. The ligand-binding pocket (LBP) of PPARγ has a large Y-shaped cavity with multiple subpockets where multiple ligands can simultaneously bind and cooperatively activate PPARγ. Focusing on this unique property of the PPARγ LBP, we describe a novel two-step cell-based strategy to develop PPARγ ligands. First, a combination of ligands that cooperatively activates PPARγ was identified using a luciferase reporter assay. Second, hybrid ligands were designed and synthesized. For proof of concept, we focused on covalent agonists, which activate PPARγ through a unique activation mechanism regulated by a covalent linkage with the Cys285 residue in the PPARγ LBP. Despite their biological significance and pharmacological potential, few covalent PPARγ agonists are known except for endogenous fatty acid metabolites. With our strategy, we determined that plant-derived cinnamic acid derivatives cooperatively activated PPARγ by combining with GW9662, an irreversible antagonist. GW9662 covalently reacts with the Cys285 residue. A docking study predicted that a cinnamic acid derivative can bind to the open cavity in GW9662-bound PPARγ LBP. On the basis of the putative binding mode, structures of both ligands were linked successfully to create a potent PPARγ agonist, which enhanced the transactivation potential of PPARγ at submicromolar levels through covalent modification of Cys285. Our approach could lead to the discovery of novel high-potency PPARγ agonists.

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“…Whether this is indeed the case is an important question since targeting secondary binding sites by fragments both in enzymes 66 and in GPCRs 67,68 has been gaining increased attention.…”

Section: Size and Shape Considerationsmentioning

confidence: 99%

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

et al. 2016

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Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function: it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry, but is sometimes used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation, and screening technologies. This compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia.Rules are for the obedience of fools and the guidance of wise men.Harry Day, Royal Air Force (1898-1977

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Multiple Fragment Docking and Linking in Primary and Secondary Pockets of Dopamine Receptors

Cited by 31 publications

References 16 publications

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification—Salvinorin A as a case study

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification—Salvinorin A as a case study

Identification of a New Type of Covalent PPARγ Agonist using a Ligand-Linking Strategy

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

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