Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer.

ABSTRACT: We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting

SUBMITTER: Ross KE 

PROVIDER: S-EPMC10089347 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature