{"doi":"10.17615/4v7n-3e65","title":"Biotin tagging coupled with amino acid-coded mass tagging for efficient and precise screening of interaction proteome in mammalian cells","abstract":"In mammalian cells, when tandem affinity purification (TAP) approach is employed, the existence of untagged endogenous target protein and repetitive washing steps together result in overall low yield of purified/stable complexes and the loss of weakly and transiently interacting partners of biological significance. To avoid the trade-offs involving in methodological sensitivity, precision, and throughput here we introduce an integrated method, biotin tagging coupled with amino acid-coded mass tagging (BioCAT) for highly sensitive and accurate screening of mammalian protein-protein interactions (PPIs). Without the need of establishing a stable cell line, using a short peptide tag which could be specifically biotinylated in vivo, the biotin-tagged target/bait protein was then isolated along with its associates efficiently by streptavidin magnetic microbeads in a single step. In a pulled-down complex amino acid-coded mass tagging (AACT) serves as ‘in-spectra’ quantitative markers to distinguish those bait-specific interactors from non-specific background proteins under stringent criteria. Applying this BioCAT approach, we first biotin-tagged in vivo a multi-functional protein family member, 14-3-3ε, which was expressed at close to endogenous level. Starting with approximately 20 millions of 293T cells which were significantly less than what needed for a TAP run, 266 specific interactors of 14-3-3ε were identified in high confidence.","journal":"UNC Libraries","year":2020,"id":117940,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9535,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":549588,"name":"Yu-Fei He","orcid":null,"position":1,"is_corresponding":false},{"id":549589,"name":"Si-Wei Tang","orcid":null,"position":2,"is_corresponding":false},{"id":541663,"name":"Ruyun Du","orcid":null,"position":3,"is_corresponding":false},{"id":548642,"name":"Xian Chen","orcid":"0000-0002-5141-7979","position":4,"is_corresponding":false},{"id":549590,"name":"Xiao-feng Xiao","orcid":null,"position":5,"is_corresponding":false},{"id":548643,"name":"Shuai Zuo","orcid":"0000-0003-2507-9555","position":6,"is_corresponding":false},{"id":411512,"name":"Pengyuan Yang","orcid":"0000-0001-5779-1008","position":7,"is_corresponding":false},{"id":540885,"name":"Huimin Bao","orcid":"0000-0003-3115-9009","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-18T23:13:54.951170Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}