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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.
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CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry–based protein–protein interaction/association data
The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.
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- Award ID(s):
- 1912837
- PAR ID:
- 10320491
- Editor(s):
- Kellogg, Doug
- Date Published:
- Journal Name:
- Molecular Biology of the Cell
- Volume:
- 32
- Issue:
- 21
- ISSN:
- 1059-1524
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1091/mbc.E21-05-0257
