Prediction of protein corona on nanomaterials by machine learning using novel descriptors.

ABSTRACT: 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 m

SUBMITTER: Duan Y 

PROVIDER: S-EPMC7043407 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature