Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model

Understanding the interaction between polymers and proteins
is
of interest for researchers in medicine, biology, food science, and
water treatment, among other fields. The goal may be to create strong
interactions with enzymes to improve their catalytic stability, while
in nanomedicine and biomedical engineering, the focus is often on
reducing protein adsorption on polymer surfaces. Researchers have
developed libraries of polymers with various monomer combinations
and tested their binding to different proteins to better understand
these interactions. In this work, we aimed to identify the polymer
with the highest or lowest binding affinity to all proteins, respectively,
using Gaussian Process Regression (GPR). However, incorporating categorical
features such as the type of monomer has not been widely applied in
GPR. Here we compare a range of process models, which were coined
Multiplicative kernel, Additive kernel, Easy to interpret Gaussian
Process model (EzGP), Latent Variable Gaussian Processes (LVGP), and
the Latent Map Gaussian Processes (LMGP) by their developers. The
LVGP model was found to perform best on the polymer–protein
data set, where the output for binding strength was given by Förster
resonance energy transfer (FRET), which can be used to help generate
large data sets for machine learning (ML). The polymer that had the
highest affinity to glucose oxidase (GOx), uricase (Uri), casein (Cas),
trypsin (Trp), carbonic anhydrase (CAn) and bovine serum albumin (BSA)
carried positive charges as well as hydrophobic benzyl groups. Negatively
charged monomers dominated the polymer that rejected the most proteins
intermixed with some cationic units, reminiscent of zwitterionic polymers.