Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts.

ABSTRACT: Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal P

SUBMITTER: Reed TJ 

PROVIDER: S-EPMC11249048 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature