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

Tavis J. Reed, Matthew D. Tyl, Alicja Tadych, Olga G. Troyanskaya, Ileana M. Cristea

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 PPIs were characterized during the reactivation from latency of the oncogenic Kaposi’s sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

Original languageEnglish (US)
Pages (from-to)488-500
Number of pages13
JournalNature Methods
Issue number3
StatePublished - Mar 2024

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology


Dive into the research topics of 'Tapioca: a platform for predicting de novo protein–protein interactions in dynamic contexts'. Together they form a unique fingerprint.

Cite this