Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy
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 geno...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05389-8 |
| _version_ | 1797795422199087104 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Aysam Guerler |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:17:42Z |
| format | Article |
| id | doaj.art-02770213d40246049a08d1048a98d4a6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-13T03:17:42Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-02770213d40246049a08d1048a98d4a62023-06-25T11:30:43ZengBMCBMC Bioinformatics1471-21052023-06-0124111310.1186/s12859-023-05389-8Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using GalaxyAysam Guerler0Dannon Baker1Marius van den Beek2Bjoern Gruening3Dave Bouvier4Nate Coraor5Stephen D. Shank6Jordan D. Zehr7Michael C. Schatz8Anton Nekrutenko9Department of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityDepartment of Biochemistry and Molecular Biology, Penn State UniversityDepartment of Bioinformatics, Freiburg UniversityDepartment of Biochemistry and Molecular Biology, Penn State UniversityDepartment of Biochemistry and Molecular Biology, Penn State UniversityInstitute for Genomics and Evolutionary Medicine, Temple UniversityInstitute for Genomics and Evolutionary Medicine, Temple UniversityDepartment of Computer Science, Johns Hopkins UniversityDepartment of Biochemistry and Molecular Biology, Penn State UniversityAbstract 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.https://doi.org/10.1186/s12859-023-05389-8Protein–protein interactionsStructural modelingGalaxy workflow |
| spellingShingle | 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 Fast and accurate genome-wide predictions and structural modeling of protein–protein interactions using Galaxy BMC Bioinformatics Protein–protein interactions Structural modeling Galaxy workflow |
| 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 | Protein–protein interactions Structural modeling Galaxy workflow |
| url | https://doi.org/10.1186/s12859-023-05389-8 |
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