Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions.
Sobia IdreesKeshav Raj PaudelPublished in: Journal of cell communication and signaling (2024)
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 ( 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.
Keyphrases
- high throughput
- mass spectrometry
- endothelial cells
- single cell
- multiple sclerosis
- healthcare
- transcription factor
- capillary electrophoresis
- rna seq
- mental health
- protein protein
- high performance liquid chromatography
- big data
- induced pluripotent stem cells
- emergency department
- machine learning
- artificial intelligence
- deep learning
- pluripotent stem cells
- drinking water
- data analysis