Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.
Galaxy workflow
Protein–protein interactions
Structural modeling
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
23 Jun 2023
23 Jun 2023
Historique:
received:
20
06
2022
accepted:
15
06
2023
medline:
26
6
2023
pubmed:
24
6
2023
entrez:
23
6
2023
Statut:
epublish
Résumé
Protein-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. Application 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. The 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.
Sections du résumé
BACKGROUND
BACKGROUND
Protein-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.
RESULTS
RESULTS
Application 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.
CONCLUSIONS
CONCLUSIONS
The 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.
Identifiants
pubmed: 37353753
doi: 10.1186/s12859-023-05389-8
pii: 10.1186/s12859-023-05389-8
pmc: PMC10288729
doi:
Substances chimiques
RNA, Viral
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
263Subventions
Organisme : NIAID NIH HHS
ID : R01 AI134384
Pays : United States
Organisme : NHGRI NIH HHS
ID : U41 HG006620
Pays : United States
Organisme : NHGRI NIH HHS
ID : U24 HG006620
Pays : United States
Organisme : NIH HHS
ID : U41 HG006620
Pays : United States
Organisme : NIH HHS
ID : R01 AI134384
Pays : United States
Informations de copyright
© 2023. The Author(s).
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