| dc.description.abstract | Molecular recognition comprises the noncovalent interaction of two or more binding partners and is central to many biological processes and designed agents for therapeutic or environmental applications. In this doctoral study, computational tools were engineered to address challenging in molecular recognition that are otherwise difficult to solve using conventional methods. The different computational tools comprise MD simulations, energy calculations, and structural analysis coupled with programs that strategize their execution. The tools have been developed and used to 1) elucidate and differentiate the binding of structurally and physicochemically similar ligands to proteins, 2) characterize modified RNA : protein interactions, 3) study and design affibody proteins with anti-amyloid properties, 4) examine the binding of toxic compounds onto montmorillonite clays, and 5) elucidate short-peptide self- and co-assembly. These computational tools can be considered as “in silico experiments” to bridge gaps between experimental observations and theory. The application of these tools have suggested potential interactions leading to biological activity and predicted stronger signaling properties of one enantiomer over the other, revealed the broader recognition of RNA binding proteins for modified RNAs, elucidated the binding and specificity of affibody proteins for amyloidogenic proteins, predicted toxic compound adsorption free energies for clays, and examined the pathways of designed peptide self- and co-assembly, which led to the discovery of novel peptide cancer drug nanocarriers with advantageous properties for bioimaging and drug delivery. | en |
