Proteome‐wide assessment of human interactome as a source of capturing domain–motif and domain‐domain interactions

Idrees, S; Paudel, KR

Proteome‐wide assessment of human interactome as a source of capturing domain–motif and domain‐domain interactions

Idrees, S

Paudel, KR

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Journal of Cell Communication and Signaling

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Field	Value	Language
dc.contributor.author	Idrees, S https://orcid.org/0000-0001-8512-7593
dc.contributor.author	Paudel, KR
dc.date.accessioned	2024-01-23T21:51:40Z
dc.date.available	2024-01-23T21:51:40Z
dc.identifier.citation	Journal of Cell Communication and Signaling
dc.identifier.issn	1873-9601
dc.identifier.issn	1873-961X
dc.identifier.uri	http://hdl.handle.net/10453/174886
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>Protein–protein interactions (PPIs) play a crucial role in various biological processes by establishing domain–motif (DMI) and domain–domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high‐throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high‐throughput methods (Yeast two‐hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP‐MS], and Co‐fractionation‐coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High‐throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (<jats:italic>p</jats:italic>‐value <0.001), establishing Y2H and AP‐MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.</jats:p>
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Journal of Cell Communication and Signaling
dc.relation.isbasedon	10.1002/ccs3.12014
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0601 Biochemistry and Cell Biology, 1103 Clinical Sciences, 1112 Oncology and Carcinogenesis
dc.subject.classification	3101 Biochemistry and cell biology
dc.subject.classification	3211 Oncology and carcinogenesis
dc.title	Proteome‐wide assessment of human interactome as a source of capturing domain–motif and domain‐domain interactions
dc.type	Journal Article
utslib.for	0601 Biochemistry and Cell Biology
utslib.for	1103 Clinical Sciences
utslib.for	1112 Oncology and Carcinogenesis
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Strength - CFI - Centre for Inflammation
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-23T21:51:36Z
pubs.publication-status	Published online

Abstract:

AbstractProtein–protein interactions (PPIs) play a crucial role in various biological processes by establishing domain–motif (DMI) and domain–domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high‐throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high‐throughput methods (Yeast two‐hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP‐MS], and Co‐fractionation‐coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High‐throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p‐value <0.001), establishing Y2H and AP‐MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.

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http://hdl.handle.net/10453/174886