Engineered solid binding peptides can be used as molecular tools for a variety of bio/nanotechnology applications, especially in interfacing biology with solid-state devices at bio/nano soft interfaces. The control of surface organization, and therefore peptide-solid interactions, is critical and involves surface phenomena such as binding, surface diffusion, and self-organization on atomically flat solids. Each of these phenomena requires the knowledge of peptide’s folding patterns which are, however, difficult to study both experimentally and computationally. Molecular dynamics, MD, has been used to computationally model peptide/solid interactions, but without information regarding the energy landscape of peptide conformations the challenge of predictive design remains. While several methods exist for finding the energy landscapes of single peptide systems, currently no approach handles multi-peptide/surface systems. Here we use Time-Varying Autoregression with Low Rank Tensors, TVART, to efficiently explore the energy landscapes of such systems, aiming to find accurate linear approximations for predictive design of peptides at bio/nano interfaces. Using TVART, with each slice representing a discrete time window, allows for temporal smoothness and high predictive accuracy. It is anticipated that some descriptions of conformation will be better suited to describe peptide conformation energy landscapes than others; based on this premise, we examined interatomic distances/adjacencies and peptide backbone torsion angles as descriptions of peptide conformation. Through such analyses, it is becoming possible to describe how peptide conformations in multi-peptide/surface systems evolve through the energy landscape and settle into energy minima (stable conformations). These conformations can then be corroborated with experimental validation of peptide self-organization on the surface using scanning probe microscopy techniques with sub-A resolutions. The combination of computational modeling and high-resolution experiments is expected to aid predictive design platforms for future applications in biosensors, bioelectronics, and logic devices.

Everyday environmental exposure can lead to the cell’s DNA to undergo double-strand breaks (DSB). If left unrepaired, these DSB are toxic to the cell’s survivability, since it can lead to the breakdown of the genetic code that is essential for the production of proteins vital to cellular life. As such, organisms have developed mechanisms to account for these common DSB situations. These mending processes are evolved to strongly combat these breaks, as failures or mistakes in the process can result in error-stricken genetic information, resulting in faulty proteins. Not only that, DSB and its repair are observed to be important to the contribution of genetic diversity, as it allows for chromosomal crossovers. This proves that the DNA repairing mechanisms to be a complex yet significantly important process for organismal life. In Escherichia coli, DSB repair processes are carried out by a trimeric protein complex, RecBCD. The mechanism behind RecBCD is not completely understood. Previous studies in the Smith lab have proposed a working model of the protein. Here, we combine both computational prediction and genetic assay to further understand the specifics of its structure-function relationship. Past experiments have identified a structural change that affects the protein’s function. Then, using a protein-protein docking algorithm, we have identified the possible conformation and interacting amino acid docking pair accounting for it. We then follow up with site-directed mutation of these pairs to test the validity of our prediction to elucidate the exact model. These experiments will allow us to further understand the conformational effects on the complex’s function. Proper understanding of RecBCD will enable us to generate new drugs to target bacterial infections whilst also reinforcing our understanding of DSB repair.

There are an estimated 300 million people worldwide who are affected by asthma, a respiratory condition characterized by inflammation and swelling of the airways. One key process during an asthmatic event is vasodilation (i.e., the widening of blood vessels) in the lungs which causes increased blood flow and inflammation, making it difficult to breathe. Processes like vasodilation are mediated by the exchange of chemical signals between cells. Crucial to advancing our knowledge and understanding of inflammatory disease progression is gaining more insight into the underlying cellular communication during complex signaling events. Current methods used to study cell signaling include 2D cell culture studies which often lack spatial biological relevance or animal models, which are not adequate for understanding inflammation in humans. We developed a user-friendly method that can be used to measure cytokine response during inflammatory processes, including vasodilation. Our device enables the creation of a model blood vessel structure, offering a simple approach to test potential drug treatments. A 3D printed device is used to form hydrogel rings in a range of sizes. The rings are comprised of cell-laden collagen I to mimic blood vessels; vasodilators and constrictors can then be introduced to the solution housing the free-standing vessels. We then collect and analyze the solution to identify the chemical signals released by the cells during treatment with the drug. By identifying key chemical signals mediating disease pathways, we can begin to target those chemical signals and develop new therapeutic treatments.

In synthetic chemistry, the direct functionalization of C-H bonds with oxygen-containing groups is a powerful strategy to efficiently synthesize molecules used in materials and therapeutics. However, current methods to accomplish such reactions suffer from limited substrate scope, selectivity, and tolerance towards other functional groups. Iron-dependent enzymes represent a promising solution to this problem, as they are known to mediate a plethora of complex oxygenation reactions in a highly selective fashion while using inexpensive and earth-abundant reagents. In prior work, we found that Fe(II) 2-oxoglutarate dependent hydroxylases (Fe(II)/2OGs) exhibit non-native oxyfunctionalization activity on olefinic amino acids. Here, we explore the ability of Fe(II)/2OGs to catalyze non-native asymmetric oxyfunctionalizations. We plan to evaluate the ability of our library of Fe(II)/2OGs to catalyze oxyfunctionalization of non-native substrates with various functional groups. Subsequently, we will optimize activity using directed evolution to arrive at a highly active and enantioselective enzyme capable of this chemistry.

SARS-CoV-2 spreads across the globe, infecting more than 128 million people and claiming over 2.7 million lives with an absence of definitive treatment up to date. Therefore, there is an immediate need to develop treatments fighting against the COVID-19 global pandemic. The goal of my project is to generate and assess an innovative treatment for the SARS-COV-2 virus infection. Our treatment formulates computationally designed proteins, and we want to evaluate its therapeutic effects using human induced pluripotent stem cell (h-iPSC) derived cell lines and organoids. The designed protein is a combinatorial cage (mosaic cage) containing spike binders previously shown to significantly inhibit SARS-CoV-2 viral infection and F-domains that were shown to activate the Tie2 pathway. The Tie2 pathway is a key regulator of vascular stability, where active Tie2 can strengthen cell-cell junctions and enhances endothelial cell survival, thus enhancing blood vessel stability. We hypothesize that the designed protein would neutralize the spike protein to block viral entry and activate the Tie2 pathway to alleviate sepsis in COVID-19 infected patients. We will test spike-binding activity and determine the activation level of the Tie2 pathway of this mosaic cage in iPSC-derived spike-overexpressing endothelial cells. We expect to measure a strong spike-binding affinity of designed proteins and strong downstream pathway signals pAKT, pERK, pFAK in designed protein-treated iPSC-derived endothelial cells. We also plan to test the mosaic cage’s activities using Kidney Organoids. If our hypothesis is correct, we will apply the findings clinically for their potential intranasal administration as a COVID-19 therapeutic.