Movassaghi, C. S. | bioRxiv Science

bioRxiv2026

Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery

Large-scale mass spectrometry-based proteomic screening could reveal cellular mechanisms of drug action at systems resolution but remains limited by experimental complexity and the difficulty of extracting insight from high-dimensional datasets. Here, we describe an end-to-end platform that combines semi-automated sample preparation, rapid LC-MS/MS, and AI agent-based data analysis to enable scalable proteomic screening. In a screen of 172 compounds in HepG2 cells, we generated 1,232 proteomes with more than 8,700 quantified proteins in approximately three weeks. Agentic AI reduced data analysis and interpretation time to less than one day while translating proteomic measurements into structured mechanism-oriented summaries and experimentally testable hypotheses. Guided by this framework, we validated: (1) a cholesterol-lowering effect of methylene blue in vitro and (2) an association between loratadine exposure and increased circulating iron in matched electronic health record analyses. This work establishes a scalable platform for generating proteomic drug perturbation data and automatically converting that data into mechanistic insights and candidate translational hypotheses using AI.

systems biology↗

bioRxiv2025

Antibiotics Rewire Core Metabolic and Ribosomal Programs in Mammalian Cells

Antibiotics are routinely added to mammalian cell culture media to prevent bacterial growth. However, the use of antibiotics in cell culture can confound downstream experimental results. While genomic and transcriptomic differences between cell cultures treated with and without antibiotics are well-documented, far fewer, if any, comprehensive proteomic comparisons on the use of antibiotics in cell culture have been performed. Here, we present a study on the proteome-wide differences of culturing HepG2 cells in antibiotic (i.e., penicillin/streptomycin) and non-antibiotic-containing media. Using a longitudinal and crossover treatment study design, we analyzed 119 samples across nine passages and four conditions. On average, 9,374 proteins were detected per sample, and we identified 383 proteins that were differentially abundant between conditions. These changes included ribosomal and mitochondrial proteins, demonstrating that off-target effects of antibiotics on mammalian cells occur at the protein level. Linear mixed-effects modeling suggested that the proteomic impact of antibiotic treatment is strongest in the first passage after treatment and stabilizes after approximately three passages. Furthermore, initiating antibiotic treatment induced a greater number of differentially abundant proteins than discontinuing treatment. Lastly, we compared our results to existing literature on the use of common antibiotics in mammalian cell culture. We identified proteins and pathways conserved across studies, -omics layers, and cell types. We hope that this detailed proteomic survey will aid researchers in comparing cross-study or cross-condition results from antibiotic-treated mammalian cells and inform appropriate experimental designs for the use of antibiotics in cell culture. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/660816v1_ufig1.gif" ALT="Figure 1"> View larger version (90K): org.highwire.dtl.DTLVardef@123d592org.highwire.dtl.DTLVardef@19a8399org.highwire.dtl.DTLVardef@135f134org.highwire.dtl.DTLVardef@6ebcbb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗