Effective in silico methods to predict protein corona compositions on engineered nanomaterials (ENMs) could help elucidate the biological outcomes of ENMs in biosystems without the need for conducting lengthy experiments for corona characterization. However, the physicochemical properties of ENMs, used as the descriptors in current modeling methods, are insufficient to represent the complex interactions between ENMs and proteins. Herein, we utilized the fluorescence change (FC) from fluorescamine labeling on a protein, with or without the presence of the ENM, as a novel descriptor of the ENM to build machine learning models for corona formation. FCs were significantly correlated with the abundance of the corresponding proteins in the corona on diverse classes of ENMs, including metal and metal oxides, nanocellulose, and 2D ENMs. Prediction models established by the random forest algorithm using FCs as the ENM descriptors showed better performance than the conventional descriptors, such as ENM size and surface charge, in the prediction of corona formation. Moreover, they were able to predict protein corona formation on ENMs with very heterogeneous properties. We believe this novel descriptor can improve in silico studies of corona formation, leading to a better understanding on the protein adsorption behaviors of diverse ENMs in different biological matrices. Such information is essential for gaining a comprehensive view of how ENMs interact with biological systems in ENM safety and sustainability assessments.

中文翻译：

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使用新颖的描述符通过机器学习预测纳米材料上的蛋白质电晕


有效预测工程纳米材料 (ENM) 上蛋白质电晕成分的计算机方法可以帮助阐明生物系统中 ENM 的生物学结果，而无需进行冗长的电晕表征实验。然而，当前建模方法中用作描述符的 ENM 的理化性质不足以代表 ENM 与蛋白质之间复杂的相互作用。在这里，我们利用蛋白质上荧光胺标记的荧光变化（FC），无论是否存在 ENM，作为 ENM 的新描述符来构建电晕形成的机器学习模型。 FC 与不同类别 ENM（包括金属和金属氧化物、纳米纤维素和 2D ENM）上电晕中相应蛋白质的丰度显着相关。使用 FC 作为 ENM 描述符的随机森林算法建立的预测模型在预测电晕形成方面表现出比传统描述符（例如 ENM 尺寸和表面电荷）更好的性能。此外，他们能够预测具有非常异质特性的 ENM 上蛋白质冠的形成。我们相信这种新颖的描述符可以改进电晕形成的计算机研究，从而更好地了解不同生物基质中不同 ENM 的蛋白质吸附行为。这些信息对于全面了解 ENM 在 ENM 安全性和可持续性评估中如何与生物系统相互作用至关重要。