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Complex+: Aided Decision-Making for the Study of Protein Complexes

View ORCID ProfileMehrnoosh Oghbaie, Petr Šulc, View ORCID ProfileDavid Fenyö, Michael Pennock, View ORCID ProfileJohn LaCava
doi: https://doi.org/10.1101/744656
Mehrnoosh Oghbaie
1Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY
2School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA
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  • For correspondence: moghbaie@rockefeller.edu jlacava@rockefeller.edu
Petr Šulc
3Center for Biological Physics, Arizona State University, Tempe, AZ, USA
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David Fenyö
4Institute for Systems Genetics, Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, USA
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Michael Pennock
2School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA
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John LaCava
1Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY
5European Research Institute for the Biology of Ageing, University Medical Center Groningen, Groningen, The Netherlands
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  • For correspondence: moghbaie@rockefeller.edu jlacava@rockefeller.edu
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Abstract

Proteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective – motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 22, 2019.
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Complex+: Aided Decision-Making for the Study of Protein Complexes
Mehrnoosh Oghbaie, Petr Šulc, David Fenyö, Michael Pennock, John LaCava
bioRxiv 744656; doi: https://doi.org/10.1101/744656
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Complex+: Aided Decision-Making for the Study of Protein Complexes
Mehrnoosh Oghbaie, Petr Šulc, David Fenyö, Michael Pennock, John LaCava
bioRxiv 744656; doi: https://doi.org/10.1101/744656

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