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Article ; Online: 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

BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

2023  Volume 13

Abstract: Abstract 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 ...

Abstract Abstract 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.
Keywords Protein–protein interactions ; Structural modeling ; Galaxy workflow ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Language English
Publishing date 2023-06-01T00:00:00Z
Publisher BMC
Document type Article ; Online
Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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