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Colloidal particles with anisotropic geometries and interactions display rich phase behavior and hence have the potential to serve as the basis of functional materials, which can tunably and reversibly self-assemble into different configurations. External fields are one design parameter that can be used to manipulate how systems of colloidal particles assemble with one another. One challenge in designing new materials using anisotropic colloidal particles is understanding how an individual particle’s various anisotropic features, like geometry, affect their overall self-assembly. Here, we present the results of simulation studies that explore the self-assembly of 2D colloidal squares with offset magnetic dipoles in the presence of an external field. Annealing simulations are used to measure the equilibrium-phase behavior of systems of these particles in the ground state, when the magnetic interactions dominate over the thermal forces of the system. We find that the magnetic properties of these systems are strongly influenced by the relative number of squares with opposite “handedness”, or chirality, that are present within the system. Systems of squares that contain equal numbers of either chirality are extremely responsive to the external field; a relatively weak external field is required to magnetize them. In contrast, systems that contain only one chirality of squares are significantly less responsive to the external field; a significantly stronger external field is required to elicit the same magnetic response. Ultimately, the differing macroscopic magnetic properties of these systems are related to their microscopic self- assembly in an external field. Simulation snapshots and ground state phase diagrams illustrate how the absence of opposite chirality squares prevents systems of these particles from leaving an energetically favorable antiparallel configuration in the presence of an external field. When opposite chirality squares are present, these magnetic particles assemble into a head-to-tail configuration, therefore inducing a magnetic state 

In fabricating new colloid-based materials via bottom-up design, particle–particle interactions are engineered to encourage the formation of the desired assemblies. One way to do this is to apply an external field, which orients magnetically polarized particles in the field direction. External fields have the advantage that they can be programmed to change in time (e.g., field rotation or toggling), tunably shifting the system away from equilibrium. Here, we apply a model for ferromagnetic colloidal rods that simulates their phase behavior in the presence of an external magnetic field with constant strength and direction. An annealing process slowly reduces the temperature during molecular dynamics simulations to estimate the system’s equilibrium configuration in the ground state when the magnetic interactions between colloidal rods dominate the thermal forces. Numerous annealing simulations are performed at various particle densities and external field strengths. In the absence of an external field, the magnetic rods assemble into antiparallel configurations. When the strength of the external field is sufficiently strong, the magnetic rods are forced to orient in the direction of the field and therefore form head-to-tail structures. The formation of a head-to-tail state is associated with a net magnetic moment that results from the collective alignment of all magnetic particles in the field direction. Furthermore, when systems of magnetic rods assemble into a head-to-tail state, they occupy more space than they do in a phase in which most rods are assembled into antiparallel configurations. Phase diagrams predict that the magnetic properties of systems of rod-like magnetic particles can switch between magnetic and nonmagnetic states by tuning not only the external field strength but also the particle density. 

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. 

Abstract Peptide self‐assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. We augment our previously‐established computational screening technique along with experimental biophysical characterization to discover 7‐mer peptides that self‐assemble into “parallelβ‐sheets”, that is,β‐sheets with N‐terminus‐to‐C‐terminus 𝛽‐strand vectors oriented in parallel. To accomplish the desiredβ‐strand organization, we applied thePepADamino acid sequence design software to the Class‐1 cross‐βspine defined by Sawaya et al. This molecular configuration includes two layers of parallelβ‐sheets stacked such that N‐terminus‐to‐C‐terminus vectors are oriented antiparallel for molecules on adjacentβ‐sheets. The first cohort ofPepADidentified peptides were examined for their fibrillation behavior in DMD/PRIME20 simulations, and the top performing sequence was selected as a prototype for a subsequent round of sequence refinement. The two rounds of design resulted in a library of eight 7‐mer peptides. In DMD/PRIME20 simulations, five of these peptides spontaneously formed fibril‐like structures with a predominantly parallel 𝛽‐sheet arrangement, two formed fibril‐like structure with <50% in parallel 𝛽‐sheet arrangement and one remained a random coil. Among the eight candidate peptides produced by PepAD and DMD/PRIME20, five were synthesized and purified. All five assembled into amyloid fibrils composed of parallelβ‐sheets based on Fourier transform infrared spectroscopy, circular dichroism, electron microscopy, and thioflavin‐T fluorescence spectroscopy measurements. 

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. 

Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C’s are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.