(9c) Computational Discovery of Plastic-Binding Peptides for Microplastic Remediation | AIChE

(9c) Computational Discovery 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. The exact amount of MNP pollution in the environment is unknown, but it has been estimated that there are 10 to 20 million tons in the top 200 meters of the Atlantic Ocean alone. MNPs have also been found in sea ice, the atmosphere, and food and water sources. The ubiquitous presence of MNPs causes many organisms, including humans, to consume MNPs daily. MNP consumption has been suggested to harm organisms due to toxic species adsorbed on the MNP surface. The environmental and health risks associated with MNPs have created a need to remove MNP pollution from the environment.

Our goal is to computationally design plastic-binding peptides as part of a larger group effort to remediate MNP pollution in bodies of water. The designed peptides will be conjugated to active particle microcleaners to help capture MNPs, conjugated to liquid crystal sensors to create a platform for characterizing MNP waste, and expressed on the surface of engineered microbes to increase cell adhesion to MNPs and promote subsequent MNP degradation. Peptides are being designed using the Peptide Binding Design (PepBD) algorithm developed in the Hall lab, which uses Metropolis Monte Carlo sampling to search for peptide sequences with high affinity for a target molecule. We aim to design peptide binders for polyethylene, polypropylene, and polystyrene, three of the most-commonly produced plastics.

We report the results for our initial plastic-binding peptide designs. We began by verifying that Amber’s GAFF2 parameters combined with partial charges calculated using ab initio methods reproduce experimental values of the density, radius of gyration, and heat capacity of the three target plastics. We then created amorphous and crystalline plastic surfaces that were subsequently used in molecular dynamics adsorption simulations of peptides known to have affinity for plastic. The adsorbed configurations of the peptides were used as starting structures for PepBD to search for other peptides that adsorb strongly to plastics. The peptides with the best scores underwent traditional molecular dynamics, well-tempered metadynamics, and steered molecular dynamics simulations to determine the interaction energies, adsorption free energies, adsorbed conformations, and desorption forces of the peptides. We found a polyethylene-binding peptide that has a free energy of adsorption and average pull-off force that is similar to that of a polyethylene-binding peptide found by others through phage display. This suggests we can computationally discover peptides with affinities that are equivalent to those of peptides discovered using phage display, thereby reducing the cost and time of finding plastic-binding peptides and potentially allowing for the discovery of peptides with even greater affinity to plastic than the peptides that are currently known. We are working with collaborators to experimentally test the computationally discovered peptide using atomic force microscopy.