AI-based Prediction of Protein Corona Composition on DNA Nanostructures

Area of Science:

• Biophysics
• Nanotechnology
• Biomedical Engineering

Background:

• DNA nanotechnology enables programmable nanoscale structure engineering.
• In vivo, DNA nanostructures form a protein corona, altering their properties.
• Controlling the protein corona is crucial for in vivo DNA nanodevice functionality.

Purpose of the Study:

• To investigate the relationship between DNA nanostructure design and protein corona composition.
• To identify key protein characteristics influencing adsorption onto DNA nanostructures.
• To develop a predictive model for protein corona formation on DNA nanostructures.

Main Methods:

• Prepared a diverse library of DNA nanostructures.
• Analyzed protein adsorption to DNA nanostructures.
• Developed a machine-learning model to predict protein enrichment based on design features and protein properties.

Main Results:

• Identified specific protein characteristics governing adsorption to DNA nanostructures.
• Successfully developed a machine-learning model for predicting protein corona composition.
• Established a link between DNA nanostructure design and in vivo protein interactions.

Conclusions:

• Understanding protein corona formation is key to programming DNA nanostructures in vivo.
• The developed model aids in designing DNA nanostructures with predictable in vivo behavior.
• This work facilitates the advancement of DNA nanodevices for biophysical and biomedical applications.
• Main_Methods: [
• Prepared a diverse library of DNA nanostructures.
• Analyzed protein adsorption to DNA nanostructures.
• Developed a machine-learning model to predict protein enrichment based on design features and protein properties.