Computational methods have the potential to significantly reduce the cost of drug discovery by providing key target-specific structural and physics-based information to guide the process of identifying and optimizing lead compounds. This dissertation will present developments in two classes of methods, each with a distinct balance of computational cost and accuracy. In the first part of this work, developments in the DFT-LOC methodology will be presented. The DFT-LOC methodology is successfully extended to the challenging problem of transition metal pKa prediction using a series of first-row hexaaqua complexes inspired by Photosystem II. The results of a second study involving the calculation of barrier heights in C-H bond activations in Cytochrome P450 and Methane Monooxyenase using QM/MM are presented. Dispersion corrections are combined with LOC corrections in order to achieve good agreement with experiment. In the second part of this dissertation, developments in molecular docking and protein structure prediction are discussed. The development of a novel algorithm for treating interloop interactions in proteins will be presented in the context of the Protein Local Optimization Program (PLOP). Finally, the details of a next-generation scoring function, called WSCORE, for molecular docking is presented along with application to MCL-1.

Construímos um modelo de caráter probabilístico para a embriogênese e a diferenciação celular, baseado nas redes genéticas, aleatoriamente conectadas, e no processo de criticalidade auto-organizada. Estudamos redes com 3, 4, 5, 10, 20, 30, 64 e 128 genes para as quais fizemos um levantamento do perfil da distribuição populacional da diversidade de atratores, e avaliamos a frequência com que foram gerados. Obtivemos resultados exatos para redes com 3 e 4 genes. Para redes maiores, foram obtidos resultados quantitativos através de varreduras aleatórias. Construímos autômatos de pilha de areia não direcionais, numa rede quadrada (L POT.2), e determinamos os máximos populacionais a eles associados para L=3, 4, 5, 6, 7, 8 e 9. Mostramos que os parâmetros do Caenorhabidits elegans, cuja historia desenvolvimental e bem conhecida, são compatíveis com o modelo proposto. / A probalistic model for Embriogenesis and Cellular Diferentiation, has been constructed, based on the random genetic networks and the self organized criticality process. Networks with 3, 4, 5, 10, 20, 30, 64, 128 genes were studied. A survey of the population distribution profile was made and their frequency of generation were evaluated. We obtained exact results for nets with 3 and 4 genes. Quantitative results were obtained for the larger ones by means of random scanning. We constructed a undirected sand pile model in a square net (L POT.2) and determined the population maxima for L = 3, 4, 5, 6, 7, 8 and 9. We showed that parameters for Caenorhabidits elegans, that has a well known developmental story, are in agreement with the proposed model.

Skeletal muscle atrophy is a common and debilitating condition that lacks an effective therapy. To address this problem, we used a systems-based discovery strategy to search for a small molecule whose mRNA expression signature negatively correlates to mRNA expression signatures of human skeletal muscle atrophy. This strategy identified a natural small molecule from tomato plants, tomatidine. Using cultured skeletal myotubes from both humans and mice, we found that tomatidine stimulated mTORC1 signaling and anabolism, leading to accumulation of protein and mitochondria, and ultimately, cell growth. Furthermore, in mice, tomatidine increased skeletal muscle mTORC1 signaling, reduced skeletal muscle atrophy, enhanced recovery from skeletal muscle atrophy, stimulated skeletal muscle hypertrophy, reduced adiposity and diet-induced obesity, and increased strength and exercise capacity. Collectively, these results identify tomatidine as a novel small molecule inhibitor of muscle atrophy. Tomatidine may have utility as a therapeutic agent or lead compound for skeletal muscle atrophy.

Source: Dissertation Abstracts International, Volume: 35-09, Section: B, page: 4342. / Thesis (Ph.D.)--The Florida State University, 1974.

Source: Dissertation Abstracts International, Volume: 36-08, Section: B, page: 3739. / Thesis (Ph.D.)--The Florida State University, 1975.

Source: Dissertation Abstracts International, Volume: 38-09, Section: B, page: 4024. / Thesis (Ph.D.)--The Florida State University, 1977.

Source: Dissertation Abstracts International, Volume: 39-06, Section: B, page: 2631. / Thesis (Ph.D.)--The Florida State University, 1978.

Source: Dissertation Abstracts International, Volume: 33-07, Section: B, page: 2960. / Thesis (Ph.D.)--The Florida State University, 1972.

Source: Dissertation Abstracts International, Volume: 34-07, Section: B, page: 3120. / Thesis (Ph.D.)--The Florida State University, 1973.

Source: Dissertation Abstracts International, Volume: 33-03, Section: B, page: 1026. / Thesis (Ph.D.)--The Florida State University, 1972.