Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR).
2015
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
(R 2 LOO =0.76 and R 2 LMO(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.
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