Evidence map›Paper›PMID 36677583›Full record

ArticleMolecules (Basel, Switzerland)2023

Identifying Dopamine D3 Receptor Ligands through Virtual Screening and Exploring the Binding Modes of Hit Compounds.

Hongshan Jin, Chengjun Wu, Rui Su, Tiemin Sun, Xingzhou Li, Chun Guo

Open access · goldAbstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Hongshan JinKey Laboratory of Structure-Based Drug Design and Discovery Ministry of Education, Department of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.ORCID 0000-0001-7582-0239
Chengjun WuKey Laboratory of Structure-Based Drug Design and Discovery Ministry of Education, Department of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.
Rui SuKey Laboratory of Structure-Based Drug Design and Discovery Ministry of Education, Department of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.
Tiemin SunKey Laboratory of Structure-Based Drug Design and Discovery Ministry of Education, Department of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.
Xingzhou LiBeijing Institute of Pharmacology and Toxicology, Beijing 100850, China.
Chun GuoKey Laboratory of Structure-Based Drug Design and Discovery Ministry of Education, Department of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.
Shenyang Pharmaceutical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dopamine D3 receptor (D3R) is an important central nervous system target for treating various neurological diseases. D3R antagonists modulate the improvement of psychostimulant addiction and relapse, while D3R agonists can enhance the response to dopaminergic stimulation and have potential applications in treating Parkinson’s disease, which highlights the importance of identifying novel D3R ligands. Therefore, we performed auto dock Vina-based virtual screening and D3R-binding-affinity assays to identify human D3R ligands with diverse structures. All molecules in the ChemDiv library (>1,500,000) were narrowed down to a final set of 37 molecules for the binding assays. Twenty-seven compounds exhibited over 50% inhibition of D3R at a concentration of 10 μM, and 23 compounds exhibited over 70% D3R inhibition at a concentration of 10 μM. Thirteen compounds exhibited over 80% inhibition of D3R at a concentration of 10 μM and the IC50 values were measured. The IC50 values of the five compounds with the highest D3R-inhibition rates ranged from 0.97 μM to 1.49 μM. These hit compounds exhibited good structural diversity, which prompted us to investigate their D3R-binding modes. After trial and error, we combined unbiased molecular dynamics simulation (MD) and molecular mechanics generalized Born surface area (MM/GBSA) binding free-energy calculations with the reported protein−ligand-binding pose prediction method using induced-fit docking (IFD) and binding pose metadynamics (BPMD) simulations into a self-consistent and computationally efficient method for predicting and verifying the binding poses of the hit ligands to D3R. Using this IFD-BPMD-MD-MM/GBSA method, we obtained more accurate and reliable D3R−ligand-binding poses than were obtained using the reported IFD-BPMD method. This IFD-BPMD-MD-MM/GBSA method provides a novel paradigm and reference for predicting and validating other protein−ligand binding poses.

Indexed as

ProteinsReceptors, Dopamine D3Binding SitesHumansLigandsMolecular Docking SimulationProtein BindingLigandsProteinsReceptors, Dopamine D3binding pose metadynamics simulationdopamine D3 receptorinduced-fit dockingmolecular dynamicsvirtual screening

Identifiers

PMID36677583
PMCPMC9862751
OpenAlexW4313572036

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.