Doctor of Philosophy

dissertationAdvances in molecular dynamics (MD) simulation methodologies have enabled researchers to explore the conformational spaces of biological macromolecules more efficiently and quickly. Specifically, the development of enhanced sampling techniques has provided researchers with well-converged conformational ensembles of small macromolecules. It has been shown that converged simulations of small ribonucleic acids (RNA) such as tetranucleotides result in the population of experimentally unknown conformations, indicating RNA force field artifacts. However, although being imperfect, the current RNA force fields have also been useful in characterizing the varied interactions of ions and ligands with RNA. In this thesis, we analyze conformational ensembles of dinucleotide monophosphates generated with different force fields and water models with the aim of pinpointing force field problems. We also utilize the current force fields to demonstrate the preferential potassium binding to a buried ion-binding site in a ribosomal RNA molecule known as GTPase Associating Center (GAC) and also to elucidate an ion-dependent step in its unfolding pathway. We further show magnesium-independency of binding of a crystallographic 2-benzimidazole ligand to the Internal Ribosome Entry Site (IRES) of Hepatitis C virus (HCV), using MD simulations and docking. Our strategy is to assess simulation results with existing experimental data, and then also use simulation results to increase insight into RNA interactions and folding. These methods allow us to identify deficiencies of some current RNA force fields

A novel hybrid method of β-turn identification in protein using binary logistic regression and neural network

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins