Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR)

Author(s): Padmaja Kamath, Alberto Fernandez, Francesc Giralt, Robert Rallo.

Journal Name: Current Topics in Medicinal Chemistry

Volume 15 , Issue 18 , 2015

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R2LOO=0.76 and R2LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.

Keywords: Cell association, Fingerprint, Multilinear regression, Nanoparticles, Physicochemical properties, Protein corona, Surface modification.

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 18
Year: 2015
Page: [1930 - 1937]
Pages: 8
DOI: 10.2174/1568026615666150506152808
Price: $58

Article Metrics

PDF: 28
HTML: 2