The process by which proteins and peptides render biological functions through molecular recognition and signal transduction. Solid-binding-peptides, SBP, utilize a similar process, e.g., in biomineralization or self-organization of solid surfaces, e.g., during interaction with single-layer materials, soft bio/nano interfaces. To de-novo design peptides that both bind and spontaneously self-assemble upon a 2D material, such as MoS2, we can adapt the Resonant Recognition Model (RRM) that assumes that the process of molecular recognition is a resonant interaction. The RRM is a process that takes residue-averaged potentials along a protein-sequence and uses Fourier analysis to transform them into constituent-frequencies that are associated with specific 3D structures of the active site of proteins. We adapt the procedure to short 12-AA long peptide. When multiple SBPs share a similar behavior, such as binding to MoS2, we can find the resonant-frequency that correlates with MoS2 binding functionality. From there, we predict new peptides that possess the resonant-frequency and test their predicted functionality for veracity. For the approach, we utilize an in-house developed dataset of several hundred thousand peptides (selected through next generation sequencing) that bind to MoS2 with varying strengths, so we can calculate their key resonant-frequencies in order to isolate which frequency is associated with the binding with MoS2. This information aids us in eliminating candidate resonant-frequencies from a well characterized peptide developed in our lab, that both binds and self-assembles on MoS2, M6-GrBP5. This allows us to narrow down which frequencies, and therefore which peptides are candidates for self-assembling on MoS2. The research is underway to verify these predictions towards developing a generalized model.

Some peptides are known to form stable secondary structures due to their occupation of lower energy states. These folded peptides theoretically have a greater information entropy upon folding, but this has not been experimentally proven. One such peptide, (LK)7, which reliably folds into an α-helix, is used as a case study here to prove that uncertainty in electron energy values increases upon formation of stable secondary structures. We use molecular dynamics, MD, simulation software from Schrodinger to create atom positional data trajectories over the evolution of (LK)7 from its extended to α-helical forms. Using Python and the SciPy ecosystem we create atom adjacency matrices of each frame of the trajectory and weight these matrices by the atoms’ respective counts of valence electrons. We then calculate and plot the information entropy and energy based on these valence electron adjacency matrices over the evolution of (LK)7. Moving forward we will also create trajectories using different data from the same MD simulation. One of these trajectories will involve weighting of atom adjacency matrices by electrons in orbitals not limited to the valence shell. Another will include the atom positional data of water molecules in the system. The last trajectory will use both modifications. Using these trajectories, we plan to experimentally prove that electron information entropy generally increases upon the folding of a peptide to a stable secondary structure. 

We are working on designing, developing, and understanding of an interesting class of magnetic meta-materials comprising of nanomagnets. These nanomagnets when arranged on square tile lattice forms a two-dimensional artificial spin ice which are important for modeling pyrochlore spin ice systems. We are currently engaged developing recipes for preparing thermally active ASI system comprised of permalloy (NiFe alloys). This will allow the system to explore the magnetic phase space configuration more efficiently and to achieve the true ground state of these many body magnetic system compared to conventional field demagnetization. We fabricate permalloy nanomagnets arrays out of thin film permalloy films (of thicknesses 10- 20 nm) following nanolithography processes involving electron beam lithography, metal mask transfer and ion milling process. The devices are fabricated on specially chosen SiN substrates which acts as diffusion barrier while performing post thermal annealing process at high temperatures. The permalloy films are initially tested for different annealing temperatures to analyze for any changes in surface morphology and magnetic properties. From this, a suitable safe range of annealing temperatures are determined and the ASI arrays of different permalloy thickness are subjected to controlled annealing. Once an optimum annealing temperature is found for safe thermal annealing, we subjected devices with different permalloy thicknesses between 10-15 nm to thermal demagnetization. The magnetic configurations in devices subjected to thermal demagnetization are imaged using magnetic force microscopy to determine their equilibrium configuration attained during annealing. Was found that for this thickness range a perfect demagnetization of ASI arrays are obtained in a temperature range of 320-350°C.

 Loss of tooth mineral, demineralization, is the root cause of dental ailments - the most prevalent health problems affecting over 90 percent of Americans. These range from white spot lesions, the earliest sign of dental demineralization, to periodontal diseases, which can lead to more serious health issues. Current restorative treatments of tooth structure and function utilize synthetic materials, e.g. amalgam, glass ionomers, and particle reinforced resin composites that lead to deposited secondary precipitates. Although these common procedures are well-established and relatively effective, their durability is limited due to lack of structural and functional integration of deposited layer with the underlying tooth. GEMSEC labs have developed a proprietary technology dubbed “peptide-guided remineralization” which enables the formation of a new mineral with protein-derived peptides. Using this technology, the lab teams have successfully restored dental hard tissues via several case studies including enamel, cementum, dentin under in vitro and in vivo conditions. Translating this technology into a daily-use product, we developed a prototype, dental lozenges, designed to aid in enamel remineralization using a biomineralizing peptide, ADP5, derived from amelogenin, the key enamel protein. Herein we aim to refine the lozenge formulation through an iterative study for enhanced durable and whitening remineralized layer. Remineralization performance of different tablet formulations were tested in artificial saliva using extracted human enamel teeth. The samples were characterized using SEM showing that the current lozenge formulation creates a new mineral layer on enamel up to 2 µm in thickness. In summary, the new lozenge artificially regenerates lost enamel on the molecular level to treat tooth decay and erosion. Developed through a simple biomimetic methodology, this prototype lozenge could be mass fabricated for the consumer dental care market and expanded to include dental varnishes, gels, and pastes.

Photovoltaic devices are a crux of renewable energy generation. Organic photovoltaic devices build on this by being flexible and easily processable. This research project aims to produce photovoltaic wires and a general process for solution-based wire coating. A thin stainless-steel wire is coated with three layers: an electron transport layer, a photoactive polymer layer, and a hole transport layer. Then this wire is wrapped with a silver counter electrode. These wrapped wires will be coated in a UV curable polymer to protect the polymer coating from degradation. This final product, a solar power generating wire, will have its photoconversion efficiency tested. Currently, our project team is characterizing the initial coated wire using scanning electron microscopy and optical microscopy to determine the effectiveness of our coating method. To supplement this, we are researching how others have tackled characterizing thin coatings for objects with small surface areas. We are also working on designing processing improvements to the wire coating device both by investigating industry wire coating techniques. We hope that our designs are an example of a scalable solution processing method for organic photovoltaic wires. Our prototype device can be used for general wire coating applications and the current photovoltaic product is an example of a potential product. In the future, this device could be used to solution process more efficient organic photovoltaic wires by leveraging different polymers and polymerization techniques.

Major Histocompatibility Complexes (MHC) are transmembrane proteins that utilize a selective binding domain to recognize peptide fragments in the cell environment and display these antigens on the cell surface. This selectivity of binding to different substrates is a feature that would be highly useful to mimic in the realm of genetically engineered peptide-solid surface binding, with broad implications applicable to engineered biomimetic systems. Our goal is to engineer selective binding biological molecules by mimicking the characteristics of the MHC-1 protein binding slot. The conventional approach to this problem applies directed evolution in a lab setting, selecting mutant MHCs with higher binding affinity against an antigen of interest. This method is resource and time intensive. Instead, we propose a machine learning approach to this problem via molecular graph convolutional neural networks (MGCNs) which are structured just like the connected atoms of input molecules. To explicitly model the MHC-peptide binding event as a graph, it is necessary to find computationally tenable representations of the MHC binding site. Prior attempts represented MHC binding alleles using only the critical contact residue positions of the MHC. This method omits the protein architecture, making it untenable as a graph encoding strategy. In this study, we evaluate alternate approaches to generate graph encodings of the two actively-binding alpha helices in the MHC-I complex and evaluate their performance on the task of predicting antigen binding affinity. We present an open Python toolset for generating graphs of MHC-I alpha helices and preliminary evaluation of their performance on a regression task on the Immune Epitope Database MHC-I dataset. The ability to generate de novo binding molecules for unique surfaces such as cancer surface proteins, viral spike proteins, metallic surfaces etc. has various use cases in: diagnostics, therapeutics, and engineered biomimetic systems.

A graphene-binding dodecapeptide, WT-GrBP5, spontaneously self-organizes on single layer graphite, which leads to a change in the electronic properties of the single atomic layer solid substrate. The peptide-2D solid hybrid system has the potential for applications in bioelectronics and biosensors. Self-organization of peptides on substrate is highly dependent on the peptide’s sequence and its conformational behavior on surfaces. To understand the molecular footprint of the peptide on graphene, it is essential to know the functional domains of the peptide that contribute to its ability to self-assemble. Here, we use alanine scan on WT-GrBP5 to analyze the contribution each amino acid has on the overall conformational landscape of the peptide and its interactions with graphene. Alanine scanning is a technique in which amino acids are replaced with alanine, to determine each amino acid’s effect on the peptide’s dynamics and conformational stability. Alanine is primarily used due to its small size and tendency to follow conformational preferences of other amino acids in a given peptide’s sequence. We ran Metadynamics simulations of the peptide and its Alanine-mutants on graphene, in order to sample the energy landscapes of the peptides in the solution as well as on graphene. Understanding the effect of certain amino acids on the peptide’s ability to assemble is crucial for identifying the molecular footprint of the peptide on the surface and how this contributes to the new physics that develops at these hybrid bio/nano interfaces. Our overall goal is to develop a predictive design model for bio/nano-interfaces for medical and technological applications in the future.