Evidence map›Paper›PMID 37353753›Full record

ArticleBMC bioinformatics2023

Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.

Aysam Guerler, Dannon Baker, Marius van den Beek, Bjoern Gruening, Dave Bouvier, Nate Coraor, Stephen D Shank, Jordan D Zehr, Michael C Schatz, Anton Nekrutenko

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

Article in BMC bioinformatics, 2023. 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
0.2field-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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

10 authors at 4 institutions in 2 countries.

Aysam GuerlerDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA. aysam.guerler@gmail.com.ORCID http://orcid.org/0000-0001-6513-2539
Dannon BakerDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Marius van den BeekDepartment of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
Bjoern GrueningDepartment of Bioinformatics, Freiburg University, Freiburg, Germany.
Dave BouvierDepartment of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
Nate CoraorDepartment of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
Stephen D ShankInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
Jordan D ZehrInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.
Michael C SchatzDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Anton NekrutenkoDepartment of Biochemistry and Molecular Biology, Penn State University, College Park, PA, USA.
Pennsylvania State University · USJohns Hopkins University · USTemple University · USUniversity of Freiburg · DE

Funding

Implementing the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)U24HG010263 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI Enis Afgan, VINCENT JAMES CAREY · 2018 to 2026
$23.8M
Democratization of Data Analysis in Life Sciences Through GalaxyU41HG006620 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON, SCHATZ, MICHAEL · 2012 to 2020
$14.4M
Democratization of Data Analysis in Life Sciences Through GalaxyU24HG006620 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI Daniel James Blankenberg, Jeremy Goecks · 2021 to 2026
$9.8M
Turning big data analysis infrastructure for HIV researchR01AI134384 · NIAID · PENNSYLVANIA STATE UNIVERSITY, THE · PI NEKRUTENKO, ANTON, POND, SERGEI L KOSAKOVSKY · 2017 to 2021
$4.2M
NHGRI NIH HHS U24 HG006620NHGRI NIH HHS U24 HG010263NHGRI NIH HHS U41 HG006620NIAID NIH HHS R01 AI134384NIH HHS R01 AI134384NIH HHS U41 HG006620
6 · The paper itself

Abstract

backgroundProtein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models.

resultsApplication of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2's non-structural protein 3. We also produced models of SARS-CoV2's spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4.

conclusionsThe presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

Indexed as

COVID-19Protein Interaction MappingHumansRNA, ViralSaccharomyces cerevisiaeSARS-CoV-2RNA, ViralGalaxy workflowProtein–protein interactionsStructural modeling

Identifiers

PMID37353753
PMCPMC10288729
OpenAlexW4381839029

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.