Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs. Complexities such as the diversity of the PC and variation with ENM properties and reaction conditions make the PC population difficult to predict. Here, we support the development of predictive models for PC populations by relating the biophysicochemical characteristics of proteins, ENMs, and solution conditions to PC formation using random forest classification. The resulting model offers a predictive analysis into the population of PC proteins in Ag ENM systems of various ENM sizes and surface coatings. With an area under the receiver operating characteristic curve of 0.83 and an F1-score of 0.81, a model with strong performance has been constructed based upon experimental data. The weighted contribution of each variable provides recommendations for mechanistic models based upon protein enrichment classification results. Protein biophysical properties such as pI and size are weighted heavily. Yet, ENM size, surface charge, and solution ionic strength also prove essential to an accurate model. The model can be readily modified and applied to other ENM PC populations. The model presented here represents the first step toward robust predictions of PC fingerprints.

中文翻译：

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机器学习可从理化特性对银纳米颗粒蛋白电晕的形成提供预测性分析†

在生物和环境系统中遇到的蛋白质与工程化的纳米材料（ENM）结合形成蛋白质电晕（PC），从而改变ENM的表面化学性质，反应性和命运。PC的多样性以及ENM特性和反应条件的变化等复杂性使PC群体难以预测。在这里，我们通过使用随机森林分类将蛋白质，ENM和溶液条件的生物物理化学特征与PC形成相关联，来支持PC种群预测模型的开发。所得模型为各种ENM大小和表面涂层的Ag ENM系统中的PC蛋白质种群提供了预测分析。在接收器工作特性曲线下的面积为0.83，F1得分为0.81的情况下，根据实验数据构建了性能强大的模型。每个变量的加权贡献为基于蛋白质富集分类结果的机械模型提供了建议。蛋白质生物物理特性（例如pI和大小）的权重很高。然而，ENM尺寸，表面电荷和溶液离子强度也被证明对精确模型至关重要。该模型可以轻松修改并应用于其他ENM PC群体。此处介绍的模型代表了对PC指纹进行可靠预测的第一步。溶液离子强度也证明对精确模型至关重要。该模型可以轻松修改并应用于其他ENM PC群体。此处介绍的模型代表了对PC指纹进行可靠预测的第一步。溶液离子强度也证明对精确模型至关重要。该模型可以轻松修改并应用于其他ENM PC群体。此处介绍的模型代表了对PC指纹进行可靠预测的第一步。