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Methods are needed to mitigate microplastic (MP) pollution to minimize their harm to the environment and human health. Given the ability of polypeptides to adsorb strongly to materials of micro- or nanometer size, plastic-binding peptides (PBPs) could help create bio-based tools for detecting, filtering, or degrading MNP pollution. However, the development of such tools is prevented by the lack of PBPs. In this work, we discover and evaluate PBPs for several common plastics by combining biophysical modeling, molecular dynamics (MD), quantum computing, and reinforcement learning. We frame peptide affinity for a given plastic through a Potts model that is a function of the amino acid sequence and then search for the amino acid sequences with the greatest predicted affinity using quantum annealing. We also use proximal policy optimization to find PBPs with a broader range of physicochemical properties, such as isoelectric point or solubility. Evaluation of the discovered PBPs in MD simulations demonstrates that the peptides have high affinity for two of the plastics: polyethylene and polypropylene. We conclude by describing how our computational approach could be paired with experimental approaches to create a nexus for designing and optimizing peptide-based tools that aid the detection, capture, or biodegradation of MPs. We thus hope that this study will aid in the fight against MP pollution. 

De novo peptide design exhibits great potential in materials engineering, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics—a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework’s effectiveness in generating peptide candidates, evaluate its efficiency for property-oriented design, and validate the candidates with molecular dynamics simulations. This quantum computing–based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies. 

Peptides that bind to inorganic materials can be used to functionalize surfaces, control crystallization, or assist ininterfacial 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. 

Crystallins comprise the protein-rich tissue of the eye lens. Of the three most common vertebrate subtypes, β-crystallins exhibit the widest degree of polydispersity due to their complex multimerization properties in situ. While polydispersity enables precise packing densities across the concentration gradient of the lens for vision, it is unclear why there is such a high degree of structural complexity within the β-crystallin subtype and what the role of this feature is in the lens. To investigate this, we first characterized β-crystallin polydispersity and then established a method to dynamically disrupt it in a process that is dependent on isoform composition and the presence of divalent cationic salts (CaCl 2 or MgCl 2 ). We used size-exclusion chromatography together with dynamic light scattering and mass spectrometry to show how high concentrations of divalent cations dissociate β-crystallin oligomers, reduce polydispersity, and shift the overall protein surface charge—properties that can be reversed when salts are removed. While the direct, physiological relevance of these divalent cations in the lens is still under investigation, our results support that specific isoforms of β-crystallin modulate polydispersity through multiple chemical equilibria and that this native state is disrupted by cation binding. This dynamic process may be essential to facilitating the molecular packing and optical function of the lens. 

Ensuring the public has a fundamental understanding of human–microbe interactions, immune responses, and vaccines is a critical challenge in the midst of a pandemic. These topics are commonly taught in undergraduate- and graduate-level microbiology and immunology courses; however, creating engaging methods of teaching these complex concepts to students of all ages is necessary to keep younger students interested when science seems hard. Building on the Tactile Teaching Tools with Guided Inquiry Learning (TTT-GIL) method we used to create an interactive lac operon molecular puzzle, we report here two TTT-GIL activities designed to engage diverse learners from middle schoolers to masters students in exploring molecular interactions within the immune system. By pairing physical models with structured activities built on the constructivist framework of Process-Oriented Guided Inquiry Learning (POGIL), TTT-GIL activities guide learners through their interaction with the model, using the Learning Cycle to facilitate construction of new concepts. Moreover, TTT-GIL activities are designed utilizing Universal Design for Learning (UDL) principles to include all learners through multiple means of engagement, representation, and action. The TTT-GIL activities reported here include a web-enhanced activity designed to teach concepts related to antibody–epitope binding and specificity to deaf and hard-of-hearing middle and high school students in a remote setting and a team-based activity that simulates the evolution of the Major Histocompatibility Complex (MHC) haplotype of a population exposed to pathogens. These activities incorporate TTT-GIL to engage learners in the exploration of fundamental immunology concepts and can be adapted for use with learners of different levels and educational backgrounds. 

Abstract Environmental and health risks posed by microplastics (MPs) have spurred numerous studies to better understand MPs' properties and behavior. Yet, we still lack a comprehensive understanding due to MP's heterogeneity in properties and complexity of plastic property evolution during aging processes. There is an urgent need to thoroughly understand the properties and behavior of MPs as there is increasing evidence of MPs' adverse health and environmental effects. In this perspective, we propose an integrated chemical engineering approach to improve our understanding of MPs. The approach merges artificial intelligence, theoretical methods, and experimental techniques to integrate existing data into models of MPs, investigate unknown features of MPs, and identify future areas of research. The breadth of chemical engineering, which spans biological, computational, and materials sciences, makes it well‐suited to comprehensively characterize MPs. Ultimately, this perspective charts a path for cross‐disciplinary collaborative research in chemical engineering to address the issue of MP pollution.