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<p>Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions, yet CF-MS data generation is of low throughput and therefore predictive models are often limited by the scarcity and limited diversity of high-quality training data. To address this, we present ProteoAutoNet, a robotic experimental platform integrated with a computational workflow for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. The integrated machine learning model incorporates targeted data augmentation to expand and diversify reliable protein interaction&#xa0;data, thereby improving model&#xa0;robustness. When applied to three thyroid cell lines, the model predicted 25,173 co-eluted proteins with an AUROC of 0.78. This analysis revealed significantly upregulated proteasome and prefoldin complexes in the&#xa0;lung metastatic follicular thyroid carcinoma cell line FTC238 compared with the normal thyroid cell line Nthy-ori 3-1. Notably, we identified a protein interaction between TGM2 and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. ProteoAutoNet provides an improved framework for investigating protein-protein interactions and uncovering interactions.</p>

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ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Mengge Lyu,
  • Pingping Hu,
  • Guangmei Zhang,
  • Kunpeng Ma,
  • Xuedong Zhang,
  • Pu Liu,
  • Sai Zhang,
  • Xiangqing Li,
  • Rui Sun,
  • Yi Chen,
  • Tiannan Guo

摘要

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions, yet CF-MS data generation is of low throughput and therefore predictive models are often limited by the scarcity and limited diversity of high-quality training data. To address this, we present ProteoAutoNet, a robotic experimental platform integrated with a computational workflow for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. The integrated machine learning model incorporates targeted data augmentation to expand and diversify reliable protein interaction data, thereby improving model robustness. When applied to three thyroid cell lines, the model predicted 25,173 co-eluted proteins with an AUROC of 0.78. This analysis revealed significantly upregulated proteasome and prefoldin complexes in the lung metastatic follicular thyroid carcinoma cell line FTC238 compared with the normal thyroid cell line Nthy-ori 3-1. Notably, we identified a protein interaction between TGM2 and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. ProteoAutoNet provides an improved framework for investigating protein-protein interactions and uncovering interactions.