Biopolymers perform the majority of essential functions necessary for life. From a small amount of components emerges considerable complexity in both structure and function. The separated timescales of dynamic processes and intricate intra- and inter-molecular interactions of these molecules necessitate the development and utilization of computational approaches for biopolymer study and nanotechnology applications. Biopolymer nanotechnology exploits the natural chemistry of biopolymers to perform novel functions at the nanoscale. Molecular dynamics is the numerical simulation of chemical entities according to the physical laws of motion and statistical mechanics. The number of atoms in biopolymers require coarse-grained methods to fully sample the dynamics of the system with reasonable resources. Accordingly, a coarse-grained molecular dynamics model for the characterization of hybrid nucleic acid-protein nanotechnology was developed. Proteins are represented as an anisotropic network model (ANM) which show good agreement with experimentally derived protein dynamics for a small computational cost. The model was subsequently applied to hybrid DNA-protein cages systems and exhibited excellent agreement with experimental results. Ongoing development efforts look to apply network models to oxDNA origami to create multiscale models for DNA origami. The network approximation will allow for detailed simulation of DNA origami association, of concern to DNA crystal and lattice formation. Identification and design of target-specific binders (aptamers) has received considerable attention on account of their diagnostic and therapeutic potential. Generated in selection cycles from extensive random libraries, biopolymer aptamers are of particular interest due to their potential non-immunogenic properties. Machine learning leverages the use of powerful statistical principles to train a model to transform an input into a desired output. Parameters of the model are iteratively adjusted according to the gradient of the cost function. An unsupervised and generative machine learning model was applied to Thrombin aptamer sequence data. From the model, sequence characteristics necessary for binding were identified and new aptamers capable of binding Thrombin were sampled and verified experimentally. Future work on the development and utilization of an unsupervised and interpretable machine learning model for unaligned sequence data is also discussed.

Transient protein-protein and protein-molecule interactions fluctuate between associated and dissociated states. They are widespread in nature and mediate most biological processes. These interactions are complex and are strongly influenced by factors such as concentration, structure, and environment. Understanding and utilizing these types of interactions is useful from both a fundamental and design perspective. In this dissertation, transient protein interactions are used as the sensing element of a biosensor for small molecule detection. This is done by using a transcription factor-small molecule pair that mediates the activation of a CRISPR/Cas12a complex. Activation of the Cas12a enzyme results in an amplified readout mechanism that is either fluorescence or paper based. This biosensor can successfully detect 9 different small molecules including antibiotics with a tuneable detection limit ranging from low µM to low nM. By combining protein and nucleic acid-based systems, this biosensor has the potential to report on almost any protein-molecule interaction, linking this to the intrinsic amplification that is possible when working with nucleic acid-based technologies. The second part of this dissertation focuses on understanding protein-molecule interactions at a more fundamental level, and, in so doing, exploring design rules required to generalize sensors like the ones described above. This is done by training a neural network algorithm with binding data from high density peptide micro arrays incubated with specific protein targets. Because the peptide sequences were chosen simply to evenly, though sparsely, represent all sequence space, the resulting network provides a comprehensive sequence/binding relationship for a given target protein. While past work had shown that this works well on the arrays, here I have explored how well the neural networks thus trained, predict sequence-dependent binding in the context of protein-protein and peptide-protein interactions. Amino acid sequences, either free in solution or embedded in protein structure, will display somewhat different binding properties than sequences affixed to the surface of a high-density array. However, the neural network trained on array sequences was able to both identify binding regions in between proteins and predict surface plasmon resonance-based binding propensities for peptides with statistically significant levels of accuracy.

The natural healing process for bone has multiple signaling cascades where several soluble factors are expressed at specific times to encourage regeneration. Human mesenchymal stromal cells (hMSCs) have three stages of osteogenic differentiation: an increase in cell number (day 1-4), early cell differentiation showing alkaline phosphatase (ALP) expression (day 5-14), and deposition of calcium and phosphate (day 14-28). The first two stages are of particular interest since cell adhesion peptides have been shown to have biological significance during these early stages of bone regeneration. However, far less is known about the temporal dependence of these signals. To mimic these complex systems, developing dynamic biomaterials has become a popular research area over the past decade. Advances in chemistry, materials science, and manufacturing have enabled the development of complex biomaterials that can mimic dynamic cues in the extracellular matrix. One specific area of interest is spatiotemporal control of multiple biomolecules; however, this has generally required diverse chemical approaches making the process difficult and impractical. To circumvent these issues, I developed a novel method that combines a photoresponsive hydrogel with single-stranded DNA to spatiotemporally control multiple biomolecules using a single conjugation scheme. Here, I describe a detailed protocol to manufacture a fully reversible, spatiotemporal platform using DNA handles. Norbornene-modified hyaluronic acid hydrogels were used to spatially control biomolecule presentation while single-stranded DNA was used to temporally control biomolecule presentation via toehold-mediated strand displacement. This platform was used to orthogonally control the presentation of multiple biomolecules with simple and complex spatial patterning, as well as control the cell morphology of hMSCs by tuning the presentation of the cell adhesion peptide RGDS. Then, this system was applied to study the temporal presentation of cell adhesion peptides and their effect on early osteogenic differentiation of hMSCs in vitro. The peptides used were RGDS, HAVDI, and OGP. OGP alone expressed higher ALP when presented from day 7-14 than day 0-7 or 0-14. When RGDS, HAVDI, and OGP were combined, there was an increase in ALP activity when HAVDI was presented from day 0-3 indicating that HAVDI plays an important role at earlier time points during osteogenic differentiation.

Tissues within the body enable proper function throughout an individual’s life. After severe injury or disease, many tissues do not fully heal without surgical intervention. The current surgical procedures aimed to repair tissues are not sufficient to fully restore functionality. To address these challenges, current research is seeking new tissue engineering approaches to promote tissue regeneration and functional recovery. Of particular interest, biomaterial scaffolds are designed to induce tissue regeneration by mimicking the biophysical and biochemical aspects of native tissue. While many scaffolds have been designed with homogenous properties, many tissues are heterogenous in nature. Thus, fabricating scaffolds that mimic these complex tissue properties is critical for inducing proper healing after injury. Within this dissertation, scaffolds were designed and fabricated to mimic the heterogenous properties of the following tissues: (1) the vocal fold, which is a complex 3D structure with spatially controlled mechanical properties; and (2) musculoskeletal tissue interfaces, which are fibrous tissues with highly organized gradients in structure and chemistry. A tri-layered hydrogel scaffold was fabricated through layer-by-layer stacking to mimic the mechanical structure of the vocal fold. Furthermore, magnetically-assisted electrospinning and thiol-norbornene photochemistry was used to fabricate fibrous scaffolds that mimic the structural and chemical organization of musculoskeletal interfacial tissues. The work presented in this dissertation further advances the tissue engineering field by using innovative techniques to design scaffolds that recapitulate the natural complexity of native tissues.

Antibodies are the immunoglobulins which are secreted by the B cells after a microbial invasion. They are stable and stays in the serum for a long time which makes them an excellent biomarker for disease diagnosis. Inflammatory bowel disease is a type of autoimmune disease where the immune system mistakenly attacks the commensal bacteria and leads to inflammation. We studied antibody response of 100 Crohn’s disease (CD), 100 ulcerative colitis (UC) and 100 healthy controls against 1,173 bacterial and 397 viral proteins. We found some anti-bacterial antibodies higher in CD compared to controls while some antibodies lower in UC compared to controls. We were able to build biomarker panels with AUCs of 0.81, 0.87, and 0.82 distinguishing CD vs. control, UC vs. control, and CD vs. UC, respectively. Subgroup analysis based on the Montreal classification revealed that penetrating CD behavior (B3), colonic CD location (L2), and extensive UC (E3) exhibited highest antibody reactivity among all patients. We also wanted to study the reason for the presence of autoantibodies in the sera of healthy individuals. A meta-analysis of 9 independent biomarker study was performed to find 77 common autoantibodies shared by healthy individuals. There was no gender bias; however, the number of autoantibodies increased with age, plateauing around adolescence. Molecular mimicry likely contributed to the elicitation of a subset of these common autoantibodies as 21 common autoantigens had 7 or more ungapped amino acid matches with viral proteins. Intrinsic properties of protein like hydrophilicity, basicity, aromaticity, and flexibility were enriched for common autoantigens. Subcellular localization and tissue expression analysis indicated the sequestration of some autoantigens from circulating autoantibodies can explain the absence of autoimmunity in these healthy individuals.

Structural-based drug discovery is becoming the essential tool for drug development withlower cost and higher efficiency compared to the conventional method. Knowledge of the three-dimensional structure of protein targets has the potential to accelerate the process for screening drug candidates. X-ray crystallography has proven to be the most used and indispensable technology in structural-based drug discovery. The provided comprehensive structural information about the interaction between the disease-related protein target and ligand can guide the chemical modification on the ligand to improve potency and selectivity. X-ray crystallography has been upgraded from traditional synchrotron to the third generation, which enabled the surge of the structural determination of macromolecular. The introduction of X-ray free electron laser further alleviated the uncertain and time-consuming crystal size optimization process and extenuated the radiation damage by “diffraction before destruction”. EV-D68 2A protease was proved to be an important pharmaceutical target for acute flaccid myelitis. This thesis reports the first atomic structure of the EV-D68 2A protease and the structuresof its two mutants, revealing it adopting N-terminal four-stranded sheets and C-terminal six-stranded ß-barrels structure, with a tightly bound zinc atom. These structures will guide the chemical modification on its inhibitor, Telaprevir. Integrin ⍺Mβ2 is an integrin with the α I-domain, related to many immunological functions including cell extravasation, phagocytosis, and immune synapse formation, so studying the molecular ligand-binding mechanism and activation mechanism of ⍺Mβ2 is of importance. This thesis uncovers the preliminary crystallization condition of ⍺Mβ2-I domain in complex with its ligand Pleiotrophin and the initial structural model. The structural model shows consistency with the previous hypothesis that the primary binding sites are metal iondependent adhesion sites on ⍺Mβ2-I domain and the thrombospondin type-1 repeat (TSR) domains of Pleiotrophin. Drug molecules with high potency and selectivity can be designed based on the reported structures of the EV-D68 2A protease and ⍺Mβ2-I domain in the future.

The severe resistance of bacteria and fungi towards common antibiotic drugs has led to the increasing prevalence of infections due to multi-drug resistant microbes, which is one of the most serious issue faced by the healthcare system worldwide. These drug-resistant bacteria have led to significant health problems and fatalities whereas drug-resistance fungi possess significant threat to humans, livestock, and crops globally. Furthermore, this drug resistance leads to the formation of biofilms, which are thick layers of microbes embedded in extracellular polymeric matrix. They adhere to both living and nonliving surfaces, making it harder to contain or eradicate these pathogens. The conventional strategy for combating these pathogenic bacteria and fungi has its limitations and new antimicrobials are constantly required to fight the growing resistant mechanisms. Hence, there is an immediate need for an alternative strategy to combat these drug-resistant isolates. Herein, this dissertation reports the development of novel potent antimicrobial agent based on tow-dimensional layered nanomaterials dispersed in biocompatible oligonucleotide, biomolecules, polymers, and surfactant. These synthesized novel nanomaterials successfully eliminated multidrug-resistant microbes with synergistic efforts of physical interaction, membrane disintegration, depolarization and intrinsic antimicrobial properties leading to cell death. These systems were highly effective against a broad spectrum of microbes including drug-resistant gram-positive, gram-negative bacteria and fungal isolates. Furthermore, they were successful in eradication of mature biofilm as well as inhibition of biofilms on several medically relevant surfaces. Overall, these novel systems have exceptional potential as a promising alternative solution in solving current problems faced by the healthcare system sue to these pathogenic microbes. For the next direction, a different avenue was explored where a novel system based on two-dimensional layered material with antibacterial properties was analyzed for enzyme-like activity. These nanomaterials with intrinsic enzyme-like properties are commonly known as nanozymes have many advantages over natural enzymes such as low cost, scalability and high stability. A class of ultra-high temperature ceramics known as metal diborides were synthesized in biocompatible surfactant followed by analysis of their enzymatic activity and antibacterial activity. Results demonstrate this novel system possesses a unique combination of exceptionally high affinity towards hydrogen peroxide and high activity per cost. Furthermore, it is extremely potent against pathogenic bacteria and has a high degree of biocompatibility. Hence, this new system opens the door for future possible applications in biomedicine with further research.

G protein-coupled receptors (GPCRs) are a large family of proteins involved in the cell signaling and regulation of many biological and pathological processes in the human body. To fully understand their functions, various approaches are needed. This work combines several techniques to advance the study of GPCRs with the overarching goal of pursuing X-ray crystallization using lipidic cubic phase (LCP). In meso, or LCP crystallization method involves imbedding the GPCR into a lipid membrane-mimetic material which spontaneously forms when monoacylglycerols (MAGs) are mixed at the correct hydration level and temperature. It provides a stable environment for GPCRs and has been established as the most common method to resolve structural details of GPCRs (Chapter 2). Yet, before crystallization, GPCRs need to be put through several rounds of optimization of the construct design, including truncation of N- and C- termini, fusing different soluble proteins, and mutating the receptor (Chapter 3). Other methods were also used to gain structural insights into GPCR interactions, such as coarse-grained molecular dynamic simulations, which showed the specific regions of interactions with cholesterol molecules imbedded in the membranes (Chapter 4). This study demonstrated β2-adrenergic receptor (β2AR), a GPCR, as a model of a cholesterol-sensitive receptor. Mutations were made to test the effect of removing specific residues of interest on cholesterol stabilization through the LCP-Tm assay, producing results that align with the simulation data. Finally, the goal of the last study is to provide a guide to identify which host lipids form stable LCP phases for different applications (Chapter 5). Small angle X-ray scattering is used to identify phases in hundreds of different precipitant conditions in the search of suitable host lipid for LCP studies. The results present a systematic overview of the compatibility of common MAGs by screening them against different precipitant solutions including varying salts and polyethylene glycol (PEG) concentrations, different PEG sizes, the presence of detergent or protein in the sample, and the addition of cholesterol. Together, these studies present a variety of methods to advance the structural studies of GPCRs using LCP

Membrane proteins act as sensors, gatekeepers and information carriers in the cell membranes. Functional engineering of these proteins is important for the development of molecular tools for biosensing, therapeutics and as components of artificial cells. However, using protein engineering to modify existing protein structures is challenging due to the limitations of structural changes and difficulty in folding polypeptides into defined protein structures. Recent studies have shown that nanoscale architectures created by DNA nanotechnology can be used to mimic various protein functions, including some membrane proteins. However, mimicking the highly sophisticated structural dynamics of membrane proteins by DNA nanostructures is still in its infancy, mainly due to lack of transmembrane DNA nanostructures that can mimic the dynamic behavior, ubiquitous to membrane proteins. Here, I demonstrate design of dynamic DNA nanostructures to mimic two important class of membrane proteins. First, I describe a DNA nanostructure that inserts through lipid membrane and dynamically reconfigures upon sensing a membrane-enclosed DNA or RNA target, thereby transducing biomolecular information across the lipid membrane similar to G-protein coupled receptors (GPCR’s). I use the non-destructive sensing property of our GPCR-mimetic nanodevice to sense cancer associated micro-RNA biomarkers inside exosomes without the need of RNA extraction and amplification. Second, I demonstrate a fully reversibly gated DNA nanopore that mimics the ligand mediated gating of ion channel proteins. The 20.4 X 20.4 nm-wide channel of the DNA nanopore allows timed delivery of folded proteins across synthetic and biological membranes. These studies represent early examples of dynamic DNA nanostructures in mimicking membrane protein functions. I envision that they will be used in synthetic biology to create artificial cells containing GPCR-like and ion channel-like receptors, in site-specific drug or vaccine delivery and highly sensitive biosensing applications.

Since the conception of DNA nanotechnology, the field has evolved towards the development of complex, dynamic 3D structures. The predictability of Watson-Crick base pairing makes DNA an unparalleled building block, and enables exceptional programmability in nanostructure shape and size. The work presented in this dissertation focuses on expanding two facets of the field: (1) introducing functionality through the incorporation of peptides to create DNA-peptide hybrid materials, and (2) the development of self-assembling DNA crystal lattices for scaffolding biomolecules. DNA nanostructures have long been proposed as drug delivery vehicles; however, they are not biocompatible because of their low stability in low salt environments and entrapment within the endosome. To address these issues, a functionalized peptide coating was designed to act as a counterion to a six-helix bundle, while simultaneously displaying numerous copies of an endosomal escape peptide to enable cytosolic delivery. This functionalized peptide coating creates a DNA-peptide hybrid material, but does not allow specific positioning or orientation of the peptides. The ability to control those aspects required the synthesis of DNA-peptide or DNA-peptide-DNA conjugates that can be incorporated into the nanostructure. The approach was utilized to produce a synbody where three peptides that bind transferrin with micromolar affinity, which were presented for multivalent binding to optimize affinity. Additionally, two DNA handle was attached to an enzymatically cleavable peptide to link two unique nanostructures. The second DNA handle was also used to constrain the peptide in a cyclic fashion to mimic the cell-adhesive conformations of RGD and PHSRN in fibronectin. The original goal of DNA nanotechnology was to use a crystalline lattice made of DNA to host proteins for their structural determination using X-ray crystallography. The work presented here takes significant steps towards achieving this goal, including elucidating design rules to control cavity size within the scaffold for accommodating guest molecules of unique sizes, approaches to improve the atomic detail of the scaffold, and strategies to modulate the symmetry of each unique lattice. Finally, this work surveys methodologies towards the incorporation of several guest molecules, with promising preliminary results that constitute a significant advancement towards the ultimate goal of the field.