Evidence map›Paper›PMID 29174398›Full record

ArticleJournal of structural biology2018

Assessment of the transmembrane domain structures in GPCR Dock 2013 models.

Ting Wang, Haiguang Liu, Yong Duan

Abstract read
In one paragraph

Article in Journal of structural biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Ting WangGenome Center, 451 East Health Science Drive, University of California, Davis, CA 95616, United States; Sichuan University of Science and Engineering, 180 Xueyuan Street, Huixing Road, Zigong 643000, Sichuan Province, China. Electronic address: twang@ucdavis.edu.
Haiguang LiuComplex Systems Division, Beijing Computational Science Research Center, 10 W. Dongbeiwang Rd, Haidian District, Beijing 100193, China.
Yong DuanGenome Center, 451 East Health Science Drive, University of California, Davis, CA 95616, United States.

Funding

AMBER force field consortium: a coherent biomolecular simulation platformR01GM079383 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI CIEPLAK, PIOTR, DUAN, YONG · 2007 to 2017
$4.7M
NIGMS NIH HHS R01 GM079383
6 · The paper itself

Abstract

The community-wide blind prediction of G-protein coupled receptor (GPCR) structures and ligand docking has been conducted three times and the quality of the models was primarily assessed by the accuracy of ligand binding modes. The seven transmembrane (TM) helices of the receptors were taken as a whole; thus the model quality within the 7TM domains has not been evaluated. Here we evaluate the 7TM domain structures in the models submitted for the last round of prediction - GPCR Dock 2013. Applying the 7 × 7 RMSD matrix analysis described in our prior work, we show that the models vary widely in prediction accuracy of the 7TM structures, exhibiting diverse structural differences from the targets. For the prediction of the 5-hydroxytryptamine receptors, the top 7TM models are rather close to the targets, which however are not ranked top by ligand-docking. On the other hand, notable deviations of the TMs are found in in the previously identified top docking models that closely resemble other receptors. We further reveal reasons of success and failure in ligand docking for the models. This current assessment not only complements the previous assessment, but also provides important insights into the current status of GPCR modeling and ligand docking.

Indexed as

Binding SitesCell MembraneErgotamineModels, MolecularMolecular Docking SimulationProtein DomainsReceptor, Serotonin, 5-HT1BReceptor, Serotonin, 5-HT2BReceptors, G-Protein-CoupledErgotamineReceptor, Serotonin, 5-HT1BReceptor, Serotonin, 5-HT2BReceptors, G-Protein-CoupledClassificationGPCRHomology modelLigand dockingRMSDTransmembrane

Identifiers

PMID29174398
PMCPMC5820147

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Registered trials

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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.