{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Reed TJ"],"funding":["Simons Foundation","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences","U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)","NIAID NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases","EIF | Stand Up To Cancer (SU2C)","U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)","CHDI Foundation","National Science Foundation (NSF)","State of New Jersey Department of Health (NJ Health)","EIF | Stand Up To Cancer","U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)","NHGRI NIH HHS","U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute","NIGMS NIH HHS","State of New Jersey Department of Health","National Science Foundation"],"pagination":["488-500"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11249048"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(3)"],"pubmed_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"],"journal":["Nature methods"],"pubmed_title":["Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts."],"pmcid":["PMC11249048"],"funding_grant_id":["R01HG005998","AI174515","3.1416","COCR23PRF019","DGE-2039656","R01GM114141","R01 GM114141","395506","R01GM071966","R01 AI174515","R01 HG005998","T32 GM007388","T32GM007388","R01 GM071966"],"pubmed_authors":["Reed TJ","Tyl MD","Tadych A","Cristea IM","Troyanskaya OG"],"additional_accession":[]},"is_claimable":false,"name":"Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts.","description":"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","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-06-02T19:15:24.351Z","creation":"2025-04-20T01:46:33.76Z"},"accession":"S-EPMC11249048","cross_references":{"pubmed":["38361019"],"doi":["10.1038/s41592-024-02179-9"]}}