bioRxiv: LLM agent interrogates proteomics datasets conversationally, retrospective runs flag issues in three studies

[bioRxiv] A bioRxiv preprint by da Silva de Araujo and co-authors introduces dataset interrogation, a conversational framework in which a researcher uses a large language model (LLM) agent to translate emerging questions into executable tests, check answers against controls, and keep code, inputs and decisions in a dated record. Applied retrospectively to four LC-MS/MS datasets, the framework exposed problems that changed the interpretation of three in-house studies: a search database lacking one of two symbionts, a swapped pair of tandem mass tag (TMT) channels, statistics inflated by non-host proteins, and false deamidation assignments caused by selecting the 13C isotope peak as monoisotopic; the same question asked of every dataset revealed failed or partial cysteine alkylation in all three, according to the authors. A public TMT dataset was reanalyzed from raw files in 24 minutes, a feasibility measure without a human comparison, the authors said; among spectra identified by both workflows, 99.2% received the same peptide assignment as in Proteome Discoverer, and protein fold changes correlated (r = 0.93). In seeded tests, a purpose-built channel assignment check detected all 216 between-strain channel swaps and none of 90 within-strain swaps, according to the authors. The authors said the results establish feasibility in a limited retrospective setting with one researcher, and that broader evaluation across users, models and datasets is required to determine general performance.