This work focuses on a novel approach to combine electrical current with cyanobacterial technology, called microbial electrophotosynthesis (MEPS). It involves using genetically modified PSII-less Synechocystis PCC 6803 cells to avoid photoinhibition, a problem that hinders green energy. In the work, a cathodic electron delivery system is employed for growth and synthesis. Photoinhibition leads to the dissipation energy and lower yield, and is a major obstacle to preventing green energy from competing with fossil fuels. However, the urgent need for alternative energy sources is driven by soaring energy consumption and rising atmospheric carbon dioxide levels. When developed, MEPS can contribute to a carbon capture technology while helping with energy demands. It is thought that if PSII electron flux can be replaced with an alternative source photosynthesis could be enhanced for more effective production. MEPS has the potential to address these challenges by serving as a carbon capture technology while meeting energy demands. The idea is to replace PSII electron flux with an alternative source, which can be enhanced for higher yields in light intensities not tolerated with PSII. This research specifically focuses on creating the initiation of electron flux between the cathode and the MEPS cells while controlling and measuring the system in real time. The successful proof-of-concept work shows that MEPS can indeed generate high-light-dependent current at intensities up to 2050 µmol photons m^‒2 s^‒1, delivering 113 µmol electrons h^‒1 mg-chl^‒1. The results were further developed to characterize redox tuning for electron delivery of flux to the photosynthetic electron transport chain and redox-based kinetic analysis to model the limitations of the MEPS system.

Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.

Exerting bias on a diverse pool of random short single stranded oligonucleotides (ODNs) by favoring binding to a specific target has led to the identification of countless high affinity aptamers with specificity to a single target. By exerting this same bias without prior knowledge of targets generates libraries to capture the complex network of molecular interactions presented in various biological states such as disease or cancer. Aptamers and enriched libraries have vast applications in bio-sensing, therapeutics, targeted drug delivery, biomarker discovery, and assay development. Here I describe a novel method of computational biophysical characterization of molecular interactions between a single aptamer and its cognate target as well as an alternative to next generation sequencing (NGS) as a readout for a SELEX-based assay. I demonstrate the capability of an artificial neural network (ANN) trained on the results of screening an aptamer against a random sampling of a combinatorial library of short synthetic 11mer peptides to accurately predict the binding intensities of that aptamer to the remainder of the combinatorial space originally sampled. This machine learned comprehensive non-linear relationship between amino acid sequence and aptamer binding to synthetic peptides can also make biologically relevant predictions for probable molecular interactions between the aptamer and its cognate target. Results of SELEX-based assays are determined by quantifying the presence and frequency of informative species after probing patient specimen. Here I show the potential of DNA microarrays to simultaneously monitor a pool of informative sequences within a diverse library with similar variability and reproducibility as NGS.

Interactions between proteins form the basis of almost all biological mechanisms. The majority of proteins perform their functions as a part of an assembled complex, rather than as an isolated species. Understanding the functional pathways of these protein complexes helps in uncovering the molecular mechanisms involved in the interactions. In this thesis, this has been explored in two fundamental ways. First, a biohybrid complex was assembled using the photosystem I (PSI) protein complex to translate the biochemical pathways into a non-cellular environment. This involved incorporating PSI on a porous antimony-doped tin oxide electrode using cytochrome c. Photocurrent was generated upon illumination of the PSI/electrode system alone at microamp/cm2 levels, with reduced oxygen apparently as the primary carrier. When the PSI/electrode system was coupled with ferredoxin, ferredoxin-NADP+ reductase (FNR), and NADP+, the resulting light-powered NADPH production was coupled to a dehydrogenase system for enzymatic carbon reduction. The results demonstrated that light-dependent reduction readily takes place. However, the pathways do not always match the biological pathways of PSI in nature. To create a complex self-assembled system such as the one involving PSI that is structurally well defined, there is a need to develop ways to guide the molecular interactions. In the second part of the thesis, this problem was approached by studying a well-defined system involving monoclonal antibodies (mAbs) binding their cognate epitope sequences to understand the molecular recognition properties associated with protein-protein interactions. This approach used a neural network model to derive a comprehensive and quantitative relationship between an amino acid sequence and its function by using sparse measurements of mAb binding to peptides on a high density peptide microarray. The resulting model can be used to predict the function of any peptide in the possible combinatorial sequence space. The results demonstrated that by training the model on just ~105 peptides out of the total combinatorial space of ~1010, the target sequences of the mAbs (cognate epitopes) can be predicted with high statistical accuracy. Furthermore, the biological relevance of the algorithm’s predictive ability has also been demonstrated.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.

Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that initiated their production. If one could read it, this information can be used to predict B cell epitopes on target antigens in order to design effective epitope driven vaccines, therapies and serological assays. Immunosignature technology captures the specific information content of serum IgG from infected and uninfected individuals on high density microarrays containing ~105 nearly random peptide sequences. Although the sequences of the peptides are chosen to evenly cover amino acid sequence space, the pattern of serum IgG binding to the array contains a consistent signature associated with each specific disease (e.g., Valley fever, influenza) among many individuals. Here, the disease specific but agnostic behavior of the technology has been explored by profiling molecular recognition information for five pathogens causing life threatening infectious diseases (e.g. DENV, WNV, HCV, HBV, and T.cruzi). This was done by models developed using a machine learning algorithm to model the sequence dependence of the humoral immune responses as measured by the peptide arrays. It was shown that the disease specific binding information could be accurately related to the peptide sequences used on the array by the machine learning (ML) models. Importantly, it was demonstrated that the ML models could identify or predict known linear epitopes on antigens of the four viruses. Moreover, the models identified potential novel linear epitopes on antigens of the four viruses (each has 4-10 proteins in the proteome) and of T.cruzi (a eukaryotic parasite which has over 12,000 proteins in its proteome). Finally, the predicted epitopes were tested in serum IgG binding assays such as ELISAs. Unfortunately, the assay results were inconsistent due to problems with peptide/surface interactions. In a separate study for the development of antibody recruiting molecules (ARMs) to combat microbial infections, 10 peptides from the high density peptide arrays were tested in IgG binding assays using sera of healthy individuals to find a set of antibody binding termini (ABT, a ligand that binds to a variable region of the IgG). It was concluded that one peptide (peptide 7) may be used as a potential ABT. Overall, these findings demonstrate the applications of the immunosignature technology ranging from developing tools to predict linear epitopes on pathogens of small to large proteomes to the identification of an ABT for ARMs.

RNA aptamers adopt tertiary structures that enable them to bind to specific ligands. This capability has enabled aptamers to be used for a variety of diagnostic, therapeutic, and regulatory applications. This dissertation focuses on the use RNA aptamers in two biological applications: (1) nucleic acid diagnostic assays and (2) scaffolding of enzymatic pathways. First, sensors for detecting arbitrary target RNAs based the fluorogenic RNA aptamer Broccoli are designed and validated. Studies of three different sensor designs reveal that toehold-initiated Broccoli-based aptasensors provide the lowest signal leakage and highest signal intensity in absence and in presence of the target RNA, respectively. This toehold-initiated design is used for developing aptasensors targeting pathogens. Diagnostic assays for detecting pathogen nucleic acids are implemented by integrating Broccoli-based aptasensors with isothermal amplification methods. When coupling with recombinase polymerase amplification (RPA), aptasensors enable detection of synthetic valley fever DNA down to concentrations of 2 fM. Integration of Broccoli-based aptasensors with nucleic acid sequence-based amplification (NASBA) enables as few as 120 copies/mL of synthetic dengue RNA to be detected in reactions taking less than three hours. Moreover, the aptasensor-NASBA assay successfully detects dengue RNA in clinical samples. Second, RNA scaffolds containing peptide-binding RNA aptamers are employed for programming the synthesis of nonribosomal peptides (NRPs). Using the NRP enterobactin pathway as a model, RNA scaffolds are developed to direct the assembly of the enzymes entE, entB, and entF from E. coli, along with the aryl-carrier protein dhbB from B. subtilis. These scaffolds employ X-shaped RNA motifs from bacteriophage packaging motors, kissing loop interactions from HIV, and peptide-binding RNA aptamers to position peptide-modified NRP enzymes. The resulting RNA scaffolds functionalized with different aptamers are designed and evaluated for in vitro production of enterobactin. The best RNA scaffold provides a 418% increase in enterobactin production compared with the system in absence of the RNA scaffold. Moreover, the chimeric scaffold, with E. coli and B. subtilis enzymes, reaches approximately 56% of the activity of the wild-type enzyme assembly. The studies presented in this dissertation will be helpful for future development of nucleic acid-based assays and for controlling protein interaction for NRPs biosynthesis.

Dielectrophoresis has been shown in the recent past to successfully separate bioparticles of very subtle differences at high resolutions using biophysical forces. In this study, we test the biophysical differences of methicillin resistant and susceptible Staph. aureus that are known to have very similar genomes by using a modified gradient insulator-based dielectrophoresis device (g-iDEP). MRSA is commonly seen in hospitals and is the leading killer of infectious bacteria, claiming the lives of around 10,000 people annually. G-iDEP improves many capabilities within the DEP field including sample size, cost, ease of use and analysis time. This is a promising foundation to creating a more clinically optimized diagnostic tool for both separation and detection of bacteria in the healthcare field. The capture on-set potential for fluorescently tagged MRSA (801 ± 34V) is higher than fluorescently tagged MSSA (610 ± 32V), resulting in a higher electrokinetic to dielectrophoretic mobility ratio for MRSA. Since the strains have proven to be genomically similar through sequencing, it is reasonable to attribute this significant biophysical difference to the added PBP2a enzyme in MRSA. These results are consistent with other bacterial studied within in this device and have proven to be reproducible.

The highly predictable structural and thermodynamic behavior of deoxynucleic acid (DNA) and ribonucleic acid (RNA) have made them versatile tools for creating artificial nanostructures over broad range. Moreover, DNA and RNA are able to interact with biological ligand as either synthetic aptamers or natural components, conferring direct biological functions to the nucleic acid devices. The applications of nucleic acids greatly relies on the bio-reactivity and specificity when applied to highly complexed biological systems.

This dissertation aims to 1) develop new strategy to identify high affinity nucleic acid aptamers against biological ligand; and 2) explore highly orthogonal RNA riboregulators in vivo for constructing multi-input gene circuits with NOT logic. With the aid of a DNA nanoscaffold, pairs of hetero-bivalent aptamers for human alpha thrombin were identified with ultra-high binding affinity in femtomolar range with displaying potent biological modulations for the enzyme activity. The newly identified bivalent aptamers enriched the aptamer tool box for future therapeutic applications in hemostasis, and also the strategy can be potentially developed for other target molecules. Secondly, by employing a three-way junction structure in the riboregulator structure through de-novo design, we identified a family of high-performance RNA-sensing translational repressors that down-regulates gene translation in response to cognate RNAs with remarkable dynamic range and orthogonality. Harnessing the 3WJ repressors as modular parts, we integrate them into biological circuits that execute universal NAND and NOR logic with up to four independent RNA inputs in Escherichia coli.

Diabesity is a global epidemic affecting millions worldwide. Diabesity is the term given to the link between obesity and Type II diabetes. It is estimated that ~90% of patients diagnosed with Type II diabetes are overweight or have struggled with excess body fat in the past. Type II diabetes is characterized by insulin resistance which is an impaired response of the body to insulin that leads to high blood glucose levels. Adipose tissue, previously thought of as an inert tissue, is now recognized as a major endocrine organ with an important role in the body's immune response and the development of chronic inflammation. It is speculated that adipose tissue inflammation is a major contributor to insulin resistance particular to Type II diabetes. This literature review explores the popular therapeutic targets and marketed drugs for the treatment of Type II diabetes and their role in decreasing adipose tissue inflammation. rAGE is currently in pre-clinical studies as a possible target to combat adipose tissue inflammation due to its relation to insulin resistance. Metformin and Pioglitazone are two drugs already being marketed that use unique chemical pathways to increase the production of insulin and/or decrease blood glucose levels. Sulfonylureas is one of the first FDA approved drugs used in the treatment of Type II diabetes, however, it has been discredited due to its life-threatening side effects. Bariatric surgery is a form of invasive surgery to rid the body of excess fat and has shown to normalize blood glucose levels. These treatments are all secondary to lifestyle changes, such as diet and exercise which can help halt the progression of Type II diabetes patients.