<p>The hydrogen bond is one of nature’s ubiquitous molecular interactions. Its role ranges from that of a static provider of structural integrity in proteins to that of a dynamic coordinate, along which excited state deactivation in sunscreen molecules is achieved. The work in this dissertation employs a supersonic expansion to collisionally cool peptide oligomers and a sunscreen chromophore to the zero-point vibrational level of their low lying conformational minima. These species are interrogated using high-resolution, conformer-specific ultraviolet and infrared laser spectroscopic techniques with the aim of elucidating their intrinsic conformational preferences, hydrogen bonding networks, and excited state deactivation mechanisms.</p><p>Synthetic foldamers are oligomers composed of non-natural building blocks, such as b- and g-amino acids. Incorporation of such residues into a peptide backbone results in secondary and tertiary structures that are distinct from those found in nature. Herein, the folding propensity of a series of mixed a/b and pure b-peptides is presented. In each case, both the left- and right-handed emergence of mixed-helical secondary structures, the 11/9- and the 12/10-helix, are observed. Next, the intrinsic conformational preferences of a series of increasingly complex asparagine-containing peptides are characterized. Asparagine, with its flexible carboxamide sidechain, is omnipresent within the prion forming domain of the misfolded proteins associated with several neurodegenerative diseases. Asparagine’s propensity for b-turn structures is discussed and compared with that of analogous peptide sequences found in nature.</p><p>Methyl anthranilate is a natural product that contains an identical electronic chromophore to the sunscreen agent, meradimate. The excited state deactivation mechanism of methyl anthranilate and its water complex is determined with extensive ultraviolet spectroscopic characterization, and is discussed within the broader context of its role as a sunscreen agent. Vibronic analysis coupled with computational results indicate extensive heavy-atom rearrangement leading to hydrogen atom dislocation, rather than full transfer, on the S₁ surface. This phenomenon is further characterized with infrared spectroscopy and theoretical modeling, in which the NH stretch is adiabatically separated from other internal coordinates. Extensive dilution of the dislocated NH stretch oscillator strength over many transitions and ~1,300 cm^-1 is predicted. These results may have implications for similar molecules, such as salicylic acid and its derivatives.</p>

<div>Deep neural networks (DNNs) have gained tremendous attention over the recent years due to their outstanding performance in solving many problems in different fields of science and technology. Currently, this field is of interest to many researchers and growing rapidly. The ability of DNNs to learn new concepts with minimal instructions facilitates applying current DNN-based methods to new problems. Here in this dissertation, three methods based on DNNs are discussed, tackling different problems in the field of computer-aided drug design.</div><div><br></div><div>The first method described addresses the problem of prediction of hydration properties from 3D structures of proteins without requiring molecular dynamics simulations. Water plays a major role in protein-ligand interactions and identifying (de)solvation contributions of water molecules can assist drug design. Two different model architectures are presented for the prediction the hydration information of proteins. The performance of the methods are compared with other conventional methods and experimental data. In addition, their applications in ligand optimization and pose prediction is shown.</div><div><br></div><div>The design of de novo molecules has always been of interest in the field of drug discovery. The second method describes a generative model that learns to derive features from protein sequences to design de novo compounds. We show how the model can be used to generate molecules similar to the known for the targets the model have not seen before and compare with benchmark generative models.</div><div><br></div><div>Finally, it is demonstrated how DNNs can learn to predict secondary structure propensity values derived from NMR ensembles. Secondary structure propensities are important in identifying flexible regions in proteins. Protein flexibility has a major role in drug-protein binding, and identifying such regions can assist in development of methods for ligand binding prediction. The prediction performance of the method is shown for several proteins with two or more known secondary structure conformations.</div>

Chimpanzee is the closest living species to modern humans. Although the differences in phenotype are striking between these two species, the difference in genomic sequences is surprisingly small. Species specific changes and positive selection have been mostly found in proteins, but ncRNAs are also involved, including the largely uncharacterized class of long ncRNAs (lncRNAs). A notable example is the Human Accelerated Region 1 (HAR1), the region in the human genome with the highest number of human specific substitutions: 18 in 118 nucleotides. HAR1 is located in a pair of overlapping lncRNAs that are expressed in a crucial period for brain development. Importantly, structural rather then sequence constraints lead to evolution of many ncRNAs. Different methods have been developed for detecting negative selection in ncRNA structures, but none thus far for positive selection. This motivated us to develop a novel method: the SSS-test (Selection on the Secondary Structure test). This novel method uses an excess of structure changing changes as a means of identifying positive selection. This is done using reports from RNAsnp, a tool that quantifies the structural effect of SNPs on RNA structures, and by applying multiple correction on the observations to generate selection scores. Insertions and deletions (indels) are dealt with separately using rank statistics and a background model. The scores for SNPs and indels are combined to calculate a final selection score for each of the input sequences, indicating the type of selection. We benchmarked the SSS-test with biological and synthetic datasets, obtaining coherent signals. We then applied it to a lncRNA database and obtained a set of 110 human lncRNAs as candidates for having evolved under adaptive evolution in humans. Although lncRNAs have poor sequence conservation, they have conserved splice sites, which provide ideal guides for orthology annotation. To provide an alternative method for assigning orthology for lncRNAs, we developed the 'buildOrthologs' tool. It uses as input a map of ortholog splice sites created by the SpliceMap tool and applies a greedy algorithm to reconstruct valid ortholog transcripts. We applied this novel approach to create a well-curated catalog of lncRNA orthologs for primate species. Finally, to understand the structural evolution of ncRNAs in full detail, we added a temporal aspect to the analysis. What was the order of mutations of a structure since its origin? This is a combinatorial problem, in which the exact mutations between ancestral and extant sequences must be put in order. For this, we developed the 'mutationOrder' tool using dynamic programming. It calculates every possible order of mutations and assigns probabilities to every path. We applied this novel tool to HAR1 as a case study and saw that the co-optimal paths that are equally likely to have occured share qualitatively comparable features. In general, they lead to stabilization of the human structure since the ancestral. We propose that this stabilization was caused by adaptive evolution. With the new methods we developed and our analysis of primate databases, we gained new knowledge about adaptive evolution of human lncRNAs.

Producing desirable firmness is important in manufacturing tofu from soybeans. This study’s objective was to explore the environmental impact (location and year) on soybean chemical components and identify the correlations between chemical composition and the firmness of tofu made from soybeans planted in three locations over two years. Seventeen soybean Plant Introductions (PI) from the USDA Soybean Germplasm Collection and eight check varieties were planted in Mississippi, Virginia and Missouri in 2017 and 2018. Protein subunit composition, protein secondary structure, phytic acid content, Ca2+ and Mg2+ content were determined. The result showed that A3 subunit content was strongly correlated with tofu firmness. Environmental factors had a significant influence on some chemical components in soybean seeds as well as tofu texture. The current study confirmed the validity of using A3 peptide as a criterion for estimating tofu firmness in both tofu manufacturing and food-grade soybean trade.

Molecular dynamics (MD) is a powerful tool that can be applied to protein folding and protein structure. MD allows for the calculation of movement, and final position, of atoms in a biomolecule. These movements can be used to investigate the pathways that allow proteins to fold into energetically favorable structures. While MD is very useful, it still has its limitations. Most notable, computing power and time are of constant concern. Protein structure is inherently important due to the direct link between the structure of a protein and its function. One of the four levels of protein structure, the secondary structure, is the first level to accommodate for the three-dimensional shape of a protein. The main driving force behind secondary structure is hydrogen bonding, which occurs between the carboxyl oxygen and the amine hydrogen of the backbone of a peptide. Determining a greater link between hydrogen bond patterns and types of secondary structure can provide more insight on how proteins fold. Because molecular dynamics allows for an atomic level view of the dynamics behind protein folding/unfolding, it becomes very useful in observing the effects of particular hydrogen bond patterns on the folding pathway and final structure formed of a protein. Using molecular dynamic simulations, a series of experiments in an attempt to alter structure, hydrogen bonding, and folding patterns, can be performed. This information can be used to better understand the driving force of secondary structure, and use the knowledge gained to manipulate these simulations to force folding events, and with that, desired secondary structure features.

No description available.

No description available.

No description available.

No / Biarylpyrimidines are characterized as selective ligands for higher-order nucleic acid structures. A concise and efficient synthesis has been devised incorporating Suzuki biaryl cross-coupling of dihalopyrimidines. Two ligand series are described based on the parent thioether 4,6-bis[4-[[2-(dimethylamino)ethyl]mercapto]-phenyl]pyrimidine (la) and amide 4,6-bis(4[(2-(dimethylamino)ethyl)carboxamido]phenyl)pyrimidine (2a) compounds. In UV thermal denaturation studies with the poly(dA)·[poly(dT)]2 triplex structure, thioethers showed stabilization of the triplex form (¿Tm ¿ 20 °C). In contrast, amides showed duplex stabilization (¿Tm ¿ 15 °C) and either negligible stabilization or specific destabilization (¿Tm = -5 °C) of the triplex structure. Full spectra of nucleic acid binding preferences were determined by competition dialysis. The strongest interacting thioether bound preferentially to the poly(dA)·[poly(dT)]2 triplex, Kapp = 1.6 x 105 M-1 (40 x Kapp for CT DNA duplex). In contrast, the strongest binding amide selected the (T2G20T2)4 quadruplex structure, Kapp = 0.31 x 105 M-1 (6.5 x Kapp for CT DNA duplex).

Current strategies for designing biomaterials involve creating materials and interfaces that interact with biomolecules, cells and tissues.  This thesis aims to investigate several bioactive surfaces, such as nanocrystalline diamond (NCD), hydroxyapatite (HA) and single crystalline titanium dioxide, in terms of material synthesis, surface functionalization and characterization. Although cochlear implants (CIs) have been proven to be clinically successful, the efficiency of these implants still needs to be improved. A CI typically only has 12-20 electrodes while the ear has approximately 3400 inner hair cells. A type of micro-textured NCD surface that consists of micrometre-sized nail-head-shaped pillars was fabricated. Auditory neurons showed a strong affinity for the surface of the NCD pillars, and the technique could be used for neural guidance and to increase the number of stimulation points, leading to CIs with improved performance. Typical transparent ceramics are fabricated using pressure-assisted sintering techniques. However, the development of a simple energy-efficient production method remains a challenge. A simple approach to fabricating translucent nano-ceramics was developed by controlling the morphology of the starting ceramic particles. Translucent nano-ceramics, including HA and strontium substituted HA, could be produced via a simple filtration process followed by pressure-less sintering. Furthermore, the application of such materials as a window material was investigated. The results show that MC3T3 cells could be observed through the translucent HA ceramic for up to 7 days. The living fluorescent staining confirmed that the MC3T3 cells were visible throughout the culture period. Single crystalline rutile possesses in vitro bioactivity, and the crystalline direction affects HA formation. The HA growth on (001), (100) and (110) faces was investigated in a simulated body fluid in the presence of fibronectin (FN) via two different processes. The HA layers on each face were analysed using different characterization techniques, revealing that the interfacial energies could be altered by the pre-adsorbed FN, which influenced HA formation. In summary, micro textured NCD, and translucent HA and FN functionalized single crystalline rutile, and their interactions with cells and biomimetic HA were studied. The results showed that controlled surface properties are important for enhancing a material’s biological performance.