School of Engineering, RMIT University, GPO Box 2476, Melbourne, Victoria, 3001, Australia.
ARC Industrial Transformation Research Hub for Australian Steel Manufacturing, Wollongong, NSW, 2522, Australia.
Sci Rep. 2019 Jan 22;9(1):265. doi: 10.1038/s41598-018-36597-5.
Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio - nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types.
防止生物污染（生物结垢）是成功开发新型表面和基于纳米粒子的技术在制造业和生物医学中的关键。蛋白质吸附是生物-纳米材料界面相互作用的关键介质，但目前对此了解甚少。尽管已经制定了一般的经验规则来指导抗蛋白质表面涂层的设计，但它们仍然主要是定性的。本文证明，通过使用机器学习方法提取材料表面化学性质与蛋白质吸附特性之间的定量关系，可以解决这一知识差距。我们说明了如何构建稳健的线性和非线性模型，以准确预测溶菌酶或纤维蛋白原等原型常见污染物在这些表面上的吸附百分比。我们的计算模型可以在测试集中以 0.82 的 r 值和 13%的预测标准误差重现蛋白质在功能化表面上的吸附。使用能够开发出 Whitesides 规则的相同数据集，我们发现了对原始规则的扩展。我们描述了一个可应用于涵盖广泛表面功能基团和蛋白质类型的大型、一致获得的数据集的工作流程。
