(346e) Computational Discovery and Evaluation of Plastic-Binding Peptides for Microplastic Remediation | AIChE

(346e) Computational Discovery and Evaluation of Plastic-Binding Peptides for Microplastic Remediation


Xiao, X., North Carolina State University
Plastic pollution on the micrometer or nanometer scale, termed microplastics or nanoplastics (MNPs), is a pressing environmental issue. There are millions of tons of MNPs in just the top 200 meters of the Atlantic Ocean, and MNPs are also found in the arctic ice caps, the atmosphere, and many food and water sources. The ubiquitous presence of MNPs causes many organisms, including humans, to consume MNPs daily, which is believed to have negative health impacts. The environmental and health risks associated with MNPs have spurred much research into creating methods that detect and remove MNP pollution from the environment.

We aim to computationally design plastic-binding peptides to help remediate MNP pollution. Specifically, we are working with collaborators to remediate MNP pollution in bodies of water by conjugating the designed peptides to active particle microcleaners to help capture MNPs, incorporating the peptides into liquid crystal sensors to create a platform for detecting and characterizing MNP waste, and expressing the peptides on the surface of engineered microbes to increase cell adhesion and subsequent degradation of MNPs.

In this presentation, we will discuss our efforts to design peptides that bind to four of the most common plastics: polyethylene, polypropylene, polystyrene, and PET. Towards this goal, we first generated models of plastic surfaces via molecular dynamics simulations, then developed a score function that captures the key intermolecular interactions between the peptide and the plastic surface. Next, we developed a procedure that produces an ensemble of adsorbed conformations to sample the vast conformational space available to peptides in the adsorbed state. We then use Peptide Binding Design (PepBD), a Monte Carlo algorithm paired with simulated annealing, to optimize the peptide sequence and structure for each starting conformation. The top-scoring peptides from the ensemble of starting states were tested using steered molecular dynamics simulations to obtain approximate adsorption free energies. The best peptides from simulations were tested experimentally through atomic force microscopy and biolayer interferometry. Our results indicate the peptides designed by PepBD have higher affinity for plastics than the peptides previously found using phage display. Lastly, we also investigate the properties that endow a peptide with high affinity for plastics, and how these properties vary for different plastic targets.