Peptides that bind to inorganic materials
can be used
to functionalize
surfaces, control crystallization, or assist in interfacial self-assembly.
In the past, inorganic-binding peptides have been found predominantly
through peptide library screening. While this method has successfully
identified peptides that bind to a variety of materials, an alternative
design approach that can intelligently search for peptides and provide
physical insight for peptide affinity would be desirable. In this
work, we develop a computational, physics-based approach to design
inorganic-binding peptides, focusing on peptides that bind to the
common plastics polyethylene, polypropylene, polystyrene, and poly(ethylene
terephthalate). The PepBD algorithm, a Monte Carlo method that samples
peptide sequence and conformational space, was modified to include
simulated annealing, relax hydration constraints, and an ensemble
of conformations to initiate design. These modifications led to the
discovery of peptides with significantly better scores compared to
those obtained using the original PepBD. PepBD scores were found to
improve with increasing van der Waals interactions, although strengthening
the intermolecular van der Waals interactions comes at the cost of
introducing unfavorable electrostatic interactions. The best designs
are enriched in amino acids with bulky side chains and possess hydrophobic
and hydrophilic patches whose location depends on the adsorbed conformation.
Future work will evaluate the top peptide designs in molecular dynamics
simulations and experiment, enabling their application in microplastic
pollution remediation and plastic-based biosensors.