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

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...

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Autores principales: Guerler, Aysam, Baker, Dannon, van den Beek, Marius, Gruening, Bjoern, Bouvier, Dave, Coraor, Nate, Shank, Stephen D., Zehr, Jordan D., Schatz, Michael C., Nekrutenko, Anton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288729/
https://www.ncbi.nlm.nih.gov/pubmed/37353753
http://dx.doi.org/10.1186/s12859-023-05389-8
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author Guerler, Aysam
Baker, Dannon
van den Beek, Marius
Gruening, Bjoern
Bouvier, Dave
Coraor, Nate
Shank, Stephen D.
Zehr, Jordan D.
Schatz, Michael C.
Nekrutenko, Anton
author_facet Guerler, Aysam
Baker, Dannon
van den Beek, Marius
Gruening, Bjoern
Bouvier, Dave
Coraor, Nate
Shank, Stephen D.
Zehr, Jordan D.
Schatz, Michael C.
Nekrutenko, Anton
author_sort Guerler, Aysam
collection PubMed
description 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: 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: 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.
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spelling pubmed-102887292023-06-24 Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy Guerler, Aysam Baker, Dannon van den Beek, Marius Gruening, Bjoern Bouvier, Dave Coraor, Nate Shank, Stephen D. Zehr, Jordan D. Schatz, Michael C. Nekrutenko, Anton BMC Bioinformatics Software 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: 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: 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. BioMed Central 2023-06-23 /pmc/articles/PMC10288729/ /pubmed/37353753 http://dx.doi.org/10.1186/s12859-023-05389-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Guerler, Aysam
Baker, Dannon
van den Beek, Marius
Gruening, Bjoern
Bouvier, Dave
Coraor, Nate
Shank, Stephen D.
Zehr, Jordan D.
Schatz, Michael C.
Nekrutenko, Anton
Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
title Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
title_full Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
title_fullStr Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
title_full_unstemmed Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
title_short Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
title_sort fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using galaxy
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288729/
https://www.ncbi.nlm.nih.gov/pubmed/37353753
http://dx.doi.org/10.1186/s12859-023-05389-8
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