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<p>When engineered nanomaterials enter biological fluids, they rapidly interact with biomolecules to form a protein corona on their surface, potentially inducing significant changes in the secondary structure of proteins. This process involves various interactions and influencing factors, but it remains unclear which factors play the most critical role in affecting protein conformation. In this study, based on extensive literature data, we employed a meta-analysis approach combined with random forest and gradient boosted decision tree (GBDT) models to systematically evaluate the influence of different factors on protein secondary structure changes during protein corona formation, and ranked their importance. The results revealed that nanoparticle incubation concentration, protein concentration, and nanoparticle size are the most critical factors. Both the random forest and GBDT models performed well, with ROC curve AUC values of 0.75 and 0.93, respectively, indicating strong predictive capabilities. Understanding the key factors influencing protein conformational changes during corona formation is essential for evaluating the biocompatibility, toxicity, and drug delivery efficiency of nanomaterials and provides a theoretical basis for designing safer and more effective nanomaterials.</p>

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Structural hijackers exposed: meta-analysis meets ensemble learning to rank nano-driven multifactorial conductors of protein corona's secondary regulators

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Chen Yan,
  • Juan Xiao,
  • Yun Cheng,
  • Xin Xia,
  • Siqi Li,
  • Xiaohui Lv,
  • Aiwen Wu,
  • Yang Bu,
  • Xin Zeng

摘要

When engineered nanomaterials enter biological fluids, they rapidly interact with biomolecules to form a protein corona on their surface, potentially inducing significant changes in the secondary structure of proteins. This process involves various interactions and influencing factors, but it remains unclear which factors play the most critical role in affecting protein conformation. In this study, based on extensive literature data, we employed a meta-analysis approach combined with random forest and gradient boosted decision tree (GBDT) models to systematically evaluate the influence of different factors on protein secondary structure changes during protein corona formation, and ranked their importance. The results revealed that nanoparticle incubation concentration, protein concentration, and nanoparticle size are the most critical factors. Both the random forest and GBDT models performed well, with ROC curve AUC values of 0.75 and 0.93, respectively, indicating strong predictive capabilities. Understanding the key factors influencing protein conformational changes during corona formation is essential for evaluating the biocompatibility, toxicity, and drug delivery efficiency of nanomaterials and provides a theoretical basis for designing safer and more effective nanomaterials.