Machine Learning for Quantitative Prediction of Protein Adsorption on Well-Defined Polymer Brush Surfaces with Diverse Chemical Properties

Polymer informatics has attracted increasing attention
because
machine learning can establish quantitative structure–property
relationships in polymer materials. Understanding and controlling
protein adsorption on polymer surfaces are crucial for various applications,
such as protein immobilization supports, biosensors, and antibiofouling
surfaces. However, protein adsorption is a complex phenomenon that
is difficult to predict quantitatively owing to the involvement of
multiple factors. Therefore, this study aims to establish a machine
learning model for protein adsorption on densely packed polymer brushes
with various chemical structures, as these surfaces are well-suited
for analyzing structure–property correlations between the polymer’s
chemical structure and adsorption amount during initial protein adsorption.
Two proteins, bovine serum albumin (BSA) and lysozyme, are adopted
as target proteins, with the expectation that differences in their
charge profiles will be reflected in the resulting machine learning
model. The descriptors of the polymer brush surfaces include their
grafted structures (thickness) and chemical properties, which are
described by the contact angle and ζ potential. This allows
physicochemical knowledge to be incorporated into the machine learning
model. Random forest exhibits the best performance in all situations,
accurately predicting the amounts of adsorbed BSA and lysozyme. In
addition, the prediction of the contact angle and ζ potential
by machine learning also enables a quantitative and explainable prediction
of protein adsorption based on theoretical molecular descriptors,
ensuring that no characteristics are overlooked. Moreover, the model
is used to analyze the contributions of electrostatic and hydrophobic
interactions to protein adsorption. In conclusion, a machine learning
model is developed to predict protein adsorption on polymer brush
surfaces, incorporating descriptors such as the grafted structure,
contact angle, and ζ potential. It provides quantitative predictions
and analyzes the roles of electrostatic and hydrophobic interactions,
advancing the design of functional polymer surfaces for applications
in biosensors and antifouling technologies.