There have been several reports that cataract development results from unregulated Ca2+ mediated degradation of lens crystallins. The calpain isoform m-calpain, a cysteine protease, is known to be a major player in cataract formation in rodent lenses and recent evidence indicates that over-activation by Ca2+ causes cataractogenesis in other mammals. Molecular modelling studies of seventeen analogues of compound SJA6017 (our lead compound) in a calpain model are compared to measured IC50 values against ovine calpain. The studies validated the potential of the ‘model’, method and defined activity criteria that could be used as a tool to select molecules to synthesize as potential calpain inhibitors. Using this screening methodology and two virtual libraries of potential inhibitory molecules led to the synthesis of several inhibitors including macrocyclic 811. In vitro sheep eye lens culture experiments showed that macrocycle 811 possessed the characteristics to slow cataractogenesis.

Geopolymers have received increasing attention as a promising sustainable alternative to ordinary Portland cement (OPC). However, the relationship among the synthesis, geopolymerization process, microstructures, molecular strucutres and mechanical properties of geopolymers remains poorly understood. To fill this knowledge gap, this dissertation focuses on the correlation of chemical composition-reaction kinetics-microstructure-mechanical properties of geopolymers. This study also sheds light on the durability, environmental impact and engineering applications of geopolymers from practical perspectives. The first part of this dissertation presents a comprehensive study on red mud-class F fly ash based geopolymers (RFFG). Firstly, RFFG with a high 28-day mechanical strength were successfully synthesized under the ambient condition of 23°C and 40 to 50% relative humidity. A nominal Na/Al molar ratio of 0.6 ~ 0.8 with a Si/Al ratio of 2 was found to be a good starting chemical composition for RFFG synthesis. Secondly, the reaction kinetics and its relation to the mechanical properties of RFFG were investigated by monitoring the development of geopolymer gels, reaction rate, porosity and mechanical properties of RFFG samples cured at room temperature, 50°C and 80°C for up to 120 days. The asymmetric stretching FTIR band of Si-O-T (T is Si or Al) centered around 960-1000 cm-1, which is the characteristic band of geopolymer gels, was observed to shift to a lower wavenumber at the early stage of the synthesis and shift to a higher wavenumber later on during the synthesis. The shift of Si-O-T band indicates that the geopolymerization took place in three stages: dissolution to Al-rich gels at Stage I, Al-rich gels to Si-rich gels at Stage II and Si-rich gels to tectosilicate networks at Stage III. The mechanical strength of RFFG barely increased, increased slowly by a limited amount and developed significantly at these three stages, respectively. An elevated curing temperature enhanced the early strength of RFFG, whereas an excessively high curing temperature resulted in a higher pore volume that offset the early-developed strength. Lastly, the remaining mechanical properties of the RFFG samples after soaking in a pH = 3.0 sulfuric acid solution for up to 120 days and the concentration of heavy metals leached from RFFG samples after the soaking were measured. The RFFG samples’ resistance against sulfuric acid was found to be comparable to that of OPC, and leaching concentrations of heavy metals were much lower than the respective EPA limits for soil contaminations. The degradation in mechanical properties of the RFFG samples during soaking in the acid was attributed primarily to the depolymerization and dealumination of geopolymer gels. The second part of this dissertation is devoted to the investigation of nano-scale mechanical properties and molecular structures of geopolymer gels with grid-nanoindentation and molecular modeling. Four phases (e.g., porous phase, partially developed geopolymer gels, geopolymer gels and unreacted metakaolin or crystals) and their nano-mechanical properties were identified in metakaolin based geopolymers (MKG) with grid-nanoindentation technique. It was found that the proportion of geopolymer gels largely determines the mechanical strength of the resulting geopolymers while other factors (e.g., pores and cracks) also play some roles in macro-scale mechanical strength of geopolymers. The final setting time of the geopolymers increased with the increase in Si/Al ratio and the decrease in Na/Al ratio, while the proportion of geopolymer gels and macro-mechanical strength of geopolymers increased with the increase in both Si/Al and Na/Al molar ratios, within the range of 1.2~1.7 and 0.6~1.0, respectively. In the molecular modeling, a combined density function theory (DFT)-molecular dynamic (MD) modeling simulation was developed to “synthesize� geopolymers. DFT simulation was used to optimize reactive aluminate and silicate monomers, which were subsequently used in reactive MD simulations to model the polymerization process and computationally synthesize geopolymer gels. The influence of Si/Al ratio and simulation temperatures on geopolymerization and resulting molecules of geopolymer gels was also examined. The computationally polymerized molecular structures of geopolymer gels were obtained. The distribution of Si4(mAl) and radial distribution fuctions of Si-O, Al-O, O-O and Na-Al for the models were compared and qualitatively agreed well with the experimental results from nuclear magnetic resonance (NMR) and neutron/X-ray pair distribution function in previous literature. Three polymerization stages: oligomerization, ring formation and condensation, were identified based on the nature of polymerization process, which were found to be affected by the temperature and Si/Al ratio. A higher temperature enhanced the reaction rate while a lower Si/Al ratio resulted in more compact geopolymer networks. The final part of this dissertation presents an experimental feasibility study of using geopolymer in shallow soil stabilization, in which a lean clay was stabilized with MKG at different concentrations. The study confirmed that MKG can be used as a soil stabilizer for clayey soils and the unconfined compressive strength, Young’s modulus and failure strain are comparable to or even better than OPC when the MKG’s concentration is higher than 11%. The binding effect of geopolymer gels on the soil particles was confirmed as the main mechanism for the improvement in mechanical properties of the stabilized soils with the scanning electron microscopy imaging, energy dispersive X-ray spectroscopy analyses and X-ray diffractometry characterization.

Molecular models allow computer simulations to predict the microscopic properties of macroscopic systems. Molecular modeling can also provide a fully understood test system for the application of theoretical methods. The power of a model lies in the accuracy of the parameter values which govern its mathematical behavior. In this work, a new software, called ParOpt, for general high dimensional non-linear optimization will be presented. The software provides a very general framework for the optimization of a wide variety of parameter sets. The software is especially powerful when applied to the difficult task of molecular model parameter optimization. Three applications of the ParOpt software, and the Nelder-Mead algorithm implemented within it, are presented: a coarse-grained (CG) water--ion model, a model for the determination of lipid bilayer structure via the interpretation of scattering data, and a reactive molecular dynamics (ReaxFF) model for oxygen and hydrogen. Each problem presents specific difficulties. The power and generality of the ParOpt software is illustrated by the successful optimization of such a diverse set of problems.

Molecular docking is a computational tool commonly applied in drug discovery projects and fundamental biological studies of protein-ligand interactions. Traditionally, molecular docking is used to address one of three following questions: (i) given a ligand molecule and a protein receptor, predict the binding mode (pose) of the ligand within the context of a receptor, (ii) screen a collection of small-molecules against a receptor and rank ligands by their likelihood of being active, and (iii) given a ligand molecule and a target receptor, predict the binding affinity of the two. Here, we focus on the first two questions, namely ranking and pose prediction. Currently, state-of-the-art docking algorithms predict poses within 2A of the native pose in a rate lower than ∼60% and in many cases, below 40%. In ranking, their ability to identify active ligands is inconsistent and generally suffers from high false-positive rate. In this thesis we present novel algorithms to enhance the ability of molecular docking to address these two questions. These algorithms do not substitute traditional docking but rather being applied on top of them to provide synergistic effect. Our algorithms improve pose predictions by 0.5-1.0A and ranking order for 23% of the targets in gold-standard benchmarks. As importantly, the algorithms improve the consistence of the posing and ranking predictions over diverse sets of targets and screening libraries. In addition to the posing and ranking, we present the pharmacophore concept. A pharmacophore is an ensemble of physiochemical descriptors associated with a biological target that elucidates common interaction patterns of ligands with that target. We introduce a novel pharmacophore inference algorithm and demonstrate its utilization in molecular docking. This thesis is outlined as follow. First we introduce the molecular docking approach for pose prediction and ranking. Second, we discuss the pharmacophore concept and present algorithms for pharmacophore inference. Third, we demonstrate the utilization of pharmacophores for pose prediction by re-scoring candidate poses generated by docking algorithms. Finally, we present algorithms to improve ranking by reducing bias in scoring functions employed by docking algorithms.

The pathway representing methionine integration and excision is an increasingly important target in drug design. Methionine aminopeptidase (MetAP), a metalloprotease responsible for cleaving the N-terminal methionine from nascent peptides, has been the object of many studies aimed to produce potential anti-bacterial, anti-fungal and anti-angiogenic agents. Though clinical trials are underway for several of these compounds, like fumagillin and CKD-731, they are still flawed based on their relatively weak inhibition and their physiological side effects. Therefore, the search for novel and potent inhibitors continues. Previous work has utilized phosphinic and phosphonic acid derivatives of methionine in co-crystallization studies with Escherichia coli MetAP (eMetAP). The aim of the research presented in this work is to study and assay various phosphorus- and sulfur-containing compounds as inhibitors and substrates in an effort to learn more about the biochemical machinery underlying MetAP catalysis. As well, we outline a predictive molecular modeling approach to MetAP inhibitor design to assist in identifying lead candidates amongst a body of possible molecular inhibitors. Ultimately, we not only hope to have identified key functional properties of molecules potentially useful as MetAP inhibitors, but also to have contributed to the knowledge base of the mechanistic features involved in this enzyme’s catalysis.

The pathway representing methionine integration and excision is an increasingly important target in drug design. Methionine aminopeptidase (MetAP), a metalloprotease responsible for cleaving the N-terminal methionine from nascent peptides, has been the object of many studies aimed to produce potential anti-bacterial, anti-fungal and anti-angiogenic agents. Though clinical trials are underway for several of these compounds, like fumagillin and CKD-731, they are still flawed based on their relatively weak inhibition and their physiological side effects. Therefore, the search for novel and potent inhibitors continues. Previous work has utilized phosphinic and phosphonic acid derivatives of methionine in co-crystallization studies with Escherichia coli MetAP (eMetAP). The aim of the research presented in this work is to study and assay various phosphorus- and sulfur-containing compounds as inhibitors and substrates in an effort to learn more about the biochemical machinery underlying MetAP catalysis. As well, we outline a predictive molecular modeling approach to MetAP inhibitor design to assist in identifying lead candidates amongst a body of possible molecular inhibitors. Ultimately, we not only hope to have identified key functional properties of molecules potentially useful as MetAP inhibitors, but also to have contributed to the knowledge base of the mechanistic features involved in this enzyme’s catalysis.

The abnormal expression or function of biological molecules, such as nucleic acids, proteins, or other small organic molecules, lead to the majority of diseases. Consequently, understanding the structure and function of these molecules through modeling can provide insight and perhaps suggest treatment for diseases. However, biologically relevant molecular phenomenon can vary vastly in the nature of their interactions and different classes of models are required to accommodate for this diversity. The objective of this thesis is to develop models for small molecules, amino acid peptides, and nucleic acids. A physical polarizable molecular mechanics model is described to accurately represent small molecules and single atom ions and applied to predict experimentally measurable thermodynamic properties such as hydration and binding free energies. A novel physical coarse-grain model based on Gay-Berne potentials and electrostatic multipoles has been developed for short peptides. The fraction of residues that adopt the alpha-helix conformation agrees with all-atom molecule dynamics results. Finally, a statistically-derived model based on sequence comparative sequence alignments is developed and applied to improve folding accuracy of RNA molecules. / text

Molecular docking is a computational tool commonly applied in drug discovery projects and fundamental biological studies of protein-ligand interactions. Traditionally, molecular docking is used to address one of three following questions: (i) given a ligand molecule and a protein receptor, predict the binding mode (pose) of the ligand within the context of a receptor, (ii) screen a collection of small-molecules against a receptor and rank ligands by their likelihood of being active, and (iii) given a ligand molecule and a target receptor, predict the binding affinity of the two. Here, we focus on the first two questions, namely ranking and pose prediction. Currently, state-of-the-art docking algorithms predict poses within 2A of the native pose in a rate lower than ∼60% and in many cases, below 40%. In ranking, their ability to identify active ligands is inconsistent and generally suffers from high false-positive rate. In this thesis we present novel algorithms to enhance the ability of molecular docking to address these two questions. These algorithms do not substitute traditional docking but rather being applied on top of them to provide synergistic effect. Our algorithms improve pose predictions by 0.5-1.0A and ranking order for 23% of the targets in gold-standard benchmarks. As importantly, the algorithms improve the consistence of the posing and ranking predictions over diverse sets of targets and screening libraries. In addition to the posing and ranking, we present the pharmacophore concept. A pharmacophore is an ensemble of physiochemical descriptors associated with a biological target that elucidates common interaction patterns of ligands with that target. We introduce a novel pharmacophore inference algorithm and demonstrate its utilization in molecular docking. This thesis is outlined as follow. First we introduce the molecular docking approach for pose prediction and ranking. Second, we discuss the pharmacophore concept and present algorithms for pharmacophore inference. Third, we demonstrate the utilization of pharmacophores for pose prediction by re-scoring candidate poses generated by docking algorithms. Finally, we present algorithms to improve ranking by reducing bias in scoring functions employed by docking algorithms.

No description available.

The green algae Chlamydomonas reinhardtii senses light through two photosensory proteins, channelrhodopsin-1 (ChR1) and channelrhodopsin-2 (ChR2). The initial discovery of these two photoreceptors introduced a new class of light-gated ion channels. ChR2 is an inwardly-rectified ion channel that is selective for cations of multiple valencies. Similar to microbial-rhodopsin ion pumps, ChR2 has a seven transmembrane domain motif that binds the chromophore all-trans retinal through a protonated Schiff base linkage. Physiologically, ChR2 functions to depolarize the membrane which initiates a signaling cascade triggering phototactic response. This fundamental property has been pivotal in pioneering the field of optogenetics, where excitable cells can be manipulated by light. ChR2 reliably causes neuronal spiking with high spatial and temporal control. Moreover, the recent discovery of new chloride-conducting channelrhodopsins (ChloCs) has further expanded the optogenetic toolbox. Although structurally similar to microbial-rhodopsin ion pumps, ChR2 undergoes more complex conformational rearrangements that lead to ion conductance. Currently, the molecular basis for ChR2 gating remains unresolved. Revealing the specific structural interactions that modulate ChR2 function have important implications in understanding the intricacies of ion transport and molecular differences between ion pumps, channels, and transporters. Here we describe a combined computational and experimental approach to elucidate the mechanism of ion conductance, channel gating, and structure-function relationship of ChR2. Our results have contributed to expanding our understanding of the fundamental properties of ion channels.