PIGNON: a protein-protein interaction-guided functional enrichment analysis for quantitative proteomics.

Background: Quantitative proteomics studies are often used to detect proteins that are differentially expressed across different experimental conditions. Functional enrichment analyses are then typically used to detect annotations, such as biological processes that are significantly enriched among such differentially expressed proteins to provide insights into the molecular impacts of the studied conditions. While common, this analytical pipeline often heavily relies on arbitrary thresholds of significance.

Proteomics characterization of mitochondrial-derived vesicles under oxidative stress.

Mitochondria share attributes of vesicular transport with their bacterial ancestors given their ability to form mitochondrial-derived vesicles (MDVs). MDVs are involved in mitochondrial quality control and their formation is enhanced with stress and may, therefore, play a potential role in mitochondrial-cellular communication. However, MDV proteomic cargo has remained mostly undefined.

Computational Identification of Human Biological Processes and Protein Sequence Motifs Putatively Targeted by SARS-CoV-2 Proteins Using Protein-Protein Interaction Networks.

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK 293 cells published by Gordon et al.