Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties.

ABSTRACT: 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 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 size and surface coatings. With an area under the receiver operating characteristic curve of 0.83 and F1-score of 0.81,

SUBMITTER: Findlay MR 

PROVIDER: S-EPMC5986185 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature