Evidence map›Paper›PMID 35715957›Full record

ArticleBiophysical journal2022

Towards generalizable predictions for G protein-coupled receptor variant expression.

Charles P Kuntz, Hope Woods, Andrew G McKee, Nathan B Zelt, Jeffrey L Mendenhall, Jens Meiler, Jonathan P Schlebach

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biophysical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 42% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

7 authors at 2 institutions in 2 countries.

Charles P KuntzDepartment of Chemistry, Indiana University, Bloomington, Indiana.
Hope WoodsDepartment of Chemistry, Vanderbilt University, Nashville, Tennessee; Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee.
Andrew G McKeeDepartment of Chemistry, Indiana University, Bloomington, Indiana.
Nathan B ZeltDepartment of Chemistry, Indiana University, Bloomington, Indiana.
Jeffrey L MendenhallDepartment of Chemistry, Vanderbilt University, Nashville, Tennessee; Chemical and Physical Biology Program, Vanderbilt University, Nashville, Tennessee.
Jens MeilerDepartment of Chemistry, Vanderbilt University, Nashville, Tennessee; Institute for Drug Discovery, Leipzig University Medical School, Leipzig, Saxony, Germany. Electronic address: jens@meilerlab.org.
Jonathan P SchlebachDepartment of Chemistry, Indiana University, Bloomington, Indiana. Electronic address: jschleba@indiana.edu.
Indiana University Bloomington · USVanderbilt University · US

Funding

Topological Energetics and the Cellular Quality Control of Integral Membrane ProteinsR01GM129261 · NIGMS · TRUSTEES OF INDIANA UNIVERSITY · PI SCHLEBACH, JONATHAN PATRICK · 2018 to 2022
$1.5M
NIGMS NIH HHS R01 GM129261
6 · The paper itself

Abstract

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of membrane proteins. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the PME of transmembrane domain variants of G protein-coupled receptors from structural and/ or evolutionary features. We show that our best-performing network, which we term the PME predictor, can recapitulate mutagenic trends within rhodopsin and can differentiate pathogenic transmembrane domain variants that cause it to misfold from those that compromise its signaling. This network also generates statistically significant predictions for the relative PME of transmembrane domain variants for another class A G protein-coupled receptor (β

Indexed as

KCNQ1 Potassium ChannelPotassium Channels, Voltage-GatedMutagenesisMutationRhodopsinKCNQ1 Potassium ChannelPotassium Channels, Voltage-GatedRhodopsin

Identifiers

PMID35715957
PMCPMC9382327
OpenAlexW4283067305

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.