ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and a multi-database search strategy

Abstract

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions. Protein complexes identified by CF-MS using different databases are typically integrated. However, this integration uses varying cutoffs, leading to inconsistencies in protein complex identification. Here, we present ProteoAutoNet, a robotic experimental platform integrating multi-database search strategy into machine learning models, for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. We then applied this workflow to map protein interaction networks in thyroid cancer cell lines, identifying significantly upregulated proteasome and prefoldin complexes in lung metastatic follicular thyroid carcinoma cell line FTC238 compared to normal thyroid cell line Nthy-ori 3-1. Notably, we identified a novel protein interaction network comprising PFAS, TGM2, and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. ProteoAutoNet provides an improved approach for investigating protein-protein interactions and uncovering novel networks, driving advancements in high-throughput proteomics research.