(637f) Computational Discovery of Plastic-Binding Peptides for Remediating Microplastic Pollution

Methods are needed to remediate microplastic (MP) pollution so as to limit potential harm to the environment and human health. One promising approach is to use polypeptides to detect and/or capture MP pollution, as they can adsorb strongly to micro- and nanomaterial sized objects. Short peptides that bind to plastics are particularly interesting because their small size means that they are more economical to manufacture than proteins. However, peptides cannot currently be applied to MP remediation because there are no known peptides that bind to the many common plastics. In this work, we aim to fill this gap by using biophysical modeling and machine learning to discover 12-residue plastic-binding peptides (PBPs) that bind strongly to the common plastics polyethylene, polystyrene, polypropylene, and PET. We have been designed PBPs using the Peptide Binding Design (PepBD) algorithm. PepBD searches for PBPs by using Metropolis Monte Carlo sampling to randomly change the amino acid sequence or structure of a starting peptide adsorbed to a plastic surface. The changes are accepted or rejected based on the change in the peptide score, a measure of the peptide-plastic interaction energy and the stability (i.e., internal energy) of the peptide in the adsorbed conformation. Over the course of sampling millions of sequences and structures, PepBD identifies peptides with high affinity to the plastic. The best PBPs designs were found in molecular dynamics (MD) simulations to bind much more strongly to the target plastic than random amino acid sequences. Experimental testing supports our simulation results for the polyethylene design; designs for the other plastics are currently being evaluated. We next designed PBPs that have affinity for plastic as well as two other important features: high solubility in water, which facilitates MP remediation in aqueous environments; and preferential binding to a target plastic over others, which can help characterize or separate components of MP waste. The predicted solubilities were significantly improved by including the CamSol solubility score in the peptide score. The binding preference of a peptide for one plastic over others was moderately increased by training a long short-term memory (LSTM) network on PepBD data to predict the peptide’s score given the amino acid sequence, and then searching for sequences with large score differences between a target plastic and an off-target plastic. By identifying PBPs for many common plastics, we hope that peptide-based methods for remediating MP pollution can begin to be developed.