Evidence map›Paper›PMID 35580077›Full record

ArticlePloS one2022

DOCKGROUND membrane protein-protein set.

Ian Kotthoff, Petras J Kundrotas, Ilya A Vakser

Abstract read
In one paragraph

Article in PloS one, 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
–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

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.

  1. Protein-Protein Docking Approach to GPCR Oligomerization.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  2. Challenges of Protein-Protein Docking of the Membrane Proteins.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  3. Dockground resource for protein recognition studies.Protein science : a publication of the Protein Society · 2022
    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

3 authors.

Ian KotthoffComputational Biology Program, The University of Kansas, Lawrence, Kansas, United States of America.
Petras J KundrotasComputational Biology Program, The University of Kansas, Lawrence, Kansas, United States of America.
Ilya A VakserComputational Biology Program, The University of Kansas, Lawrence, Kansas, United States of America.ORCID 0000-0002-5743-2934

Funding

Integrated Resource for Protein Recognition StudiesR01GM074255 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI VAKSER, ILYA · 2005 to 2024
$6.2M
NIGMS NIH HHS R01 GM074255
6 · The paper itself

Abstract

Membrane proteins are significantly underrepresented in Protein Data Bank despite their essential role in cellular mechanisms and the major progress in experimental protein structure determination. Thus, computational approaches are especially valuable in the case of membrane proteins and their assemblies. The main focus in developing structure prediction techniques has been on soluble proteins, in part due to much greater availability of the structural data. Currently, structure prediction of protein complexes (protein docking) is a well-developed field of study. However, the generic protein docking approaches are not optimal for the membrane proteins because of the differences in physicochemical environment and the spatial constraints imposed by the membranes. Thus, docking of the membrane proteins requires specialized computational methods. Development and benchmarking of the membrane protein docking approaches has to be based on high-quality sets of membrane protein complexes. In this study we present a new dataset of 456 non-redundant alpha helical binary interfaces. The set is significantly larger and more representative than the previously developed sets. In the future, it will become the basis for the development of docking and scoring benchmarks, similar to the ones for soluble proteins in the Dockground resource http://dockground.compbio.ku.edu.

Indexed as

Membrane ProteinsComputational BiologyDatabases, ProteinMolecular Docking SimulationProtein BindingSoftwareMembrane Proteins

Identifiers

PMID35580077
PMCPMC9113569

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.