Molecular recognition forms the basis of all protein interactions, and therefore is crucial for maintaining biological functions and pathways. It can be governed by many factors, but in case of proteins and peptides, the amino acids sequences of the interacting entities play a huge role. It is molecular recognition that helps a protein identify the correct sequences residues necessary for an interaction, among the vast number of possibilities from the combinatorial sequence space. Therefore, it is fundamental to study how the interacting amino acid sequences define the molecular interactions of proteins. In this work, sparsely sampled peptide sequences from the combinatorial sequence space were used to study the molecular recognition observed in proteins, especially monoclonal antibodies. A machine learning based approach was used to study the molecular recognition characteristics of 11 monoclonal antibodies, where a neural network (NN) was trained on data from protein binding experiments performed on high-throughput random-sequence peptide microarrays. The use of random-sequence microarrays allowed for the peptides to be sparsely sampled from sequence space. Post-training, a sequence vs. binding relationship was deduced by the NN, for each antibody. This in silico relationship was then extended to larger libraries of random peptides, as well as to the biologically relevant sequences (target antigens, and proteomes). The NN models performed well in predicting the pertinent interactions for 6 out of the 11 monoclonal antibodies, in all aspects. The interactions of the other five monoclonal antibodies could not be predicted well by the models, due to their poor recognition of the residues that were omitted from the array. Furthermore, NN predicted sequence vs. binding relationships for 3 other proteins were experimentally probed using surface plasmon resonance (SPR). This was done to explore the relationship between the observed and predicted binding to the arrays and the observed binding on different assay platforms. It was noted that there was a general motif dependent correlation between predicted and SPR-measured binding. This study also indicated that a combined reiterative approach using in silico and in vitro techniques is a powerful tool for optimizing the selectivity of the protein-binding peptides.

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.

Efficient light collection and utilization are highly needed for developing effective photonic devices and materials. Nature is the master of organizing photosynthetic pigments into a densely packed state without self-quenching and conducting efficient energy transfer in a directed manner via implementing sophisticated proteins as scaffolds. The natural light-harvesting complex inspires the design of artificial photonic systems by utilizing synthetic templates to control the spatial arrangement and energy landscape of photoactive components. The self-assembled DNA nanostructures are highly programmable and intrinsically addressable, which makes them excellent templates for the precise organization of chromophores with desired complexity as artificial light-harvesting systems and photonic nanodevices for efficient photon capture and excitation energy transport. This dissertation focuses on the fundamental understanding and rational engineering of a series of artificial excitonic systems using programmable DNA architectures as templates to direct the self-assembly of cyanine dye aggregates. First, the DNA-templated pseudoisocyanine (PIC) dye aggregates were systematically studied to explore the effect of sequence and length of DNA templates on their excitonic properties. The results revealed that the PIC dye aggregates enable energy transfer along a defined track. Next, the benzothiazole cyanine dye K21 was introduced to form dye aggregates on double-stranded DNA templates. The strong inter-molecular coupling and weak sequence dependency of the K21 aggregates make it possible to mediate the efficient directional energy transfer over a distance up to 30 nm. Finally, the DNA helix-bundle structures with extended size and complicated geometries were employed to organize K21 dye as the scalable, addressable, and programmable excitonic complexes conducting sub-micron-scale directional exciton transport and serving as robust and modular building blocks to construct higher-order excitonic architectures.

Liquid-phase exfoliation (LPE) is a straightforward and scalable method of producing two-dimensional nanomaterials. The LPE process has typical been applied to layered van der Waals (vdW) solids, such as graphite and transition metal dichalcogenides, which have layers held together by weak van der Waals interactions. However, recent research has shown that solids with stronger bonds and non-layered structures can be converted to solution-stabilized nanosheets via LPE, some of which have shown to have interesting optical, magnetic, and photocatalytic properties. In this work, two classes of non-vdW solids – hexagonal metal diborides and boron carbide – are investigated for their morphological features, their chemical and crystallographic compositions, and their solvent preference for exfoliation. Spectroscopic and microscopic techniques are used to verify the composition and crystal structure of metal diboride nanosheets. Their application as mechanical fillers is demonstrated by incorporation into polymer nanocomposite films of polyvinyl alcohol and by successful integration into liquid photocurable 3D printing resins. Application of Hansen solubility theory to two metal diboride compositions enables extrapolation of their affinities for certain solvents and is also used to find solvent blends suitable for the nanosheets. Boron carbide nanosheets are examined for their size and thickness and their exfoliation planes are computationally analyzed and experimentally investigated using high-resolution transmission electron microscopy. The resulting analyses indicate that the exfoliation of boron carbide leads to multiple observed exfoliation planes upon LPE processing. Overall, these studies provide insight into the production and applications of LPE-produced nanosheets derived from non-vdW solids and suggest their potential application as mechanical fillers in polymer nanocomposites.

The increasing pervasiveness of infections caused by multidrug-resistant bacteria (MDR) is a major global health issue that has been further exacerbated by the dearth of antibiotics developed over the past 40 years. Drug-resistant bacteria have led to significant morbidity and mortality, and ever-increasing antibiotic resistance threatens to reverse many of the medical advances enabled by antibiotics over the last 40 years. The traditional strategy for combating these superbugs involves the development of new antibiotics. Yet, only two new classes of antibiotics have been introduced to the clinic over the past two decades, and both failed to combat broad spectrum gram-negative bacteria. This situation demands alternative strategies to combat drug-resistant superbugs. Herein, these dissertation reports the development of potent antibacterials based on biomolecule-encapsulated two-dimensional inorganic materials, which combat multidrug-resistant bacteria using alternative mechanisms of strong physical interactions with bacterial cell membrane. These systems successfully eliminate all members of the ‘Superbugs’ set of pathogenic bacteria, which are known for developing antibiotic resistance, providing an alternative to the limited ‘one bug-one drug’ approach that is conventionally used. Furthermore, these systems demonstrate a multimodal antibacterial killing mechanism that induces outer membrane destabilization, unregulated ion movement across the membranes, induction of oxidative stress, and finally apoptotic-like cell death. In addition, a peptide-encapsulation of the two-dimensional material successfully eliminated biofilms and persisters at micromolar concentrations. Overall, these novel systems have great potential as next-generation antimicrobial agents for eradication of broad spectrum multidrug-resistant bacteria.

Ultrasonication-mediated liquid-phase exfoliation has emerged as an efficient method for producing large quantities of two-dimensional materials such as graphene, boron nitride, and transition metal dichalcogenides. This thesis explores the use of this process to produce a new class of boron-rich, two-dimensional materials, namely metal diborides, and investigate their properties using bulk and nanoscale characterization methods. Metal diborides are a class of structurally related materials that contain hexagonal sheets of boron separated by metal atoms with applications in superconductivity, composites, ultra-high temperature ceramics and catalysis. To demonstrate the utility of these materials, chromium diboride was incorporated in polyvinyl alcohol as a structural reinforcing agent. These composites not only showed mechanical strength greater than the polymer itself, but also demonstrated superior reinforcing capability to previously well-known two-dimensional materials. Understanding their dispersion behavior and identifying a range of efficient dispersing solvents is an important step in identifying the most effective processing methods for the metal diborides. This was accomplished by subjecting metal diborides to ultrasonication in more than thirty different organic solvents and calculating their surface energy and Hansen solubility parameters. This thesis also explores the production and covalent modification of pristine, unlithiated molybdenum disulfide using ultrasonication-mediated exfoliation and subsequent diazonium functionalization. This approach allows a variety of functional groups to be tethered on the surface of molybdenum disulfide while preserving its semiconducting properties. The diazonium chemistry is further exploited to attach fluorescent proteins on its surface making it amenable to future biological applications. Furthermore, a general approach for delivery of anticancer drugs using pristine two-dimensional materials is also detailed here. This can be achieved by using two-dimensional materials dispersed in a non-ionic and biocompatible polymer, as nanocarriers for delivering the anticancer drug doxorubicin. The potency of this supramolecular assembly for certain types of cancer cell lines can be improved by using folic-acid-conjugated polymer as a dispersing agent due to strong binding between folic acid present on the nanocarriers and folate receptors expressed on the cells. These results show that ultrasonication-mediated liquid-phase exfoliation is an effective method for facilitating the production and diverse application of pristine two-dimensional metal diborides and transition metal dichalcogenides.