Fragment assembly methods represent the state of the art in computational protein structure prediction. However, one limitation of these methods, particularly for larger protein structures, is inadequate conformational sampling. This thesis describes studies aimed at uncovering potential causes of ineffective sampling, and the development of methods to try and address these problems. To identify behaviours that might lead to poor conformational sampling, we developed measures to study fragment-based sampling trajectories. Applying these measures to the Rosetta Abinitio and EdaFold methods showed similarities and differences in the ways that these methods make predictions, and pointed to common limitations. In both protocols, structural features such as alpha-helices were more frequently altered during the search, as compared with regions such as loops. Analyses of the fragment libraries used by these methods showed that fragments covering loop regions were less likely to possess native-like structural features, and this likely exacerbated the problems of inadequate sampling in these regions. Inadequate loop sampling leads to poor fold-level exploration within individual runs of methods such as Rosetta, and this necessitates the use of many independent runs. Guided by these findings, we developed new heuristic-based search algorithms. These algorithms were designed to facilitate the exploration of multiple energy basins within runs. Over many runs, the enhanced exploration in our protocols produced decoy sets with larger fractions of native-like solutions as compared to runs of Rosetta. Experiments with different fragment sets indicated that our methods could better translate increased fragment set quality into improvements in predictive accuracy distributions. These improvements depend most strongly on the ability of search algorithms to reliably generate native-like structures using a fragment set. In contrast, inadequate retention of native-like decoys when associated with unfavourable score values appears to be less of an issue. This thesis shows that targeted developments in conformational sampling strategies can improve the accuracy and reliability of predictions. With effective conformational sampling methods, developments in methods for fragment set construction and other areas may more reliably enhance predictive ability.

Of the 20 standard amino acids, cysteines are the only amino acids that have a reactive sulphur atom, thus enabling two cysteines to form strong covalent bonds known as disulfide bonds. Even though almost all proteins have cysteines, not all of them have disulfide bonds. Disulfide bonds provide structural stability to proteins and hence are an important constraint in determining the structure of a protein. As a result, disulfide bonds are used to study various protein properties, one of them being protein folding. Protein structure prediction is the problem of predicting the three-dimensional structure of a protein from its one-dimensional amino acid sequence. Ab initio methods are a group of methods that attempt to solve this problem from first principles, using only basic physico-chemical properties of proteins. These methods use structure libraries of short amino acid fragments in the process of predicting the structure of a protein. The protein structures from which these structure libraries are created are not classified in any other way apart from being non-redundant. In this thesis, we investigate the structural dissimilarities of short amino acid fragments when occurring in proteins with disulfide bonds and when occurring in those proteins without disulfide bonds. We are interested in this because, as mentioned earlier, the protein structures from which the structure libraries of ab initio methods are created, are not classified in any form. This means that any significant structural difference in amino acids and short fragments when occurring in proteins with and without disulfide bonds would remain unnoticed as these structure libraries have both fragments from proteins with disulfide bonds and without disulfide bonds together. Our investigation of structural dissimilarities of amino acids and short fragments is done in four phases. In phase one, by statistically analysing the phi and psi backbone dihedral angle distributions we show that these fragments have significantly different structures in terms of dihedral angles when occurring in proteins with and without disulfide bonds. In phase two, using directional statistics we investigate how structurally different are the 20 different amino acids and the short fragments when occurring in proteins with and without disulfide bonds. In phase three of our work, we investigate the differences in secondary structure preference of the 20 amino acids in proteins with and without disulfide bonds. In phase four, we further investigate and show that there are significant differences within the same secondary structure region of amino acids when they occur in proteins with and without disulfide bonds. Finally, we present the design and implementation details of a dihedral angle and secondary structure database of short amino acid fragments (DASSD) that is publicly available. Thus, in this thesis we show previously unknown significant structure differences in terms of backbone dihedral angles and secondary structures in amino acids and short fragments when they occur in proteins with and without disulfide bonds.

No description available.

Diamond-like semiconductors (DLSs) are a class of semiconductor materials having structures similar to that of either cubic or hexagonal diamond. These normal valence compounds are of interest for their wide variety of technologically useful properties that can be tuned for specific applications. Until recently, DLS research has been focused on binary and ternary compositions due to their relative ease of synthesis. However, quaternary DLSs have gained considerable popularity due to their increased compositional flexibility and their potential as multifunctional materials. Despite their growing reputation, the vast number of possible combinations and conceivable solid solutions, DLSs remain fairly unexplored.<br>This work focuses on quaternary DLSs of the formula Ag2-II-IV-S4 in order to advance the knowledge of structure-property relationships for this entire class of materials. Toward this goal, a more complete understanding of the crystal structures of these materials is necessary. This task is often problematic due to the presence of isoelectronic, or nearly isoelectonic elements, that can complicate X-ray structure refinements. In this work, Ag2CdGeS4 is used as a case study to demonstrate that this problem can be resolved with careful consideration of bonding environments as well as the use of high-resolution X-ray sources. For the novel DLS Ag2ZnSiS4, the relationship between the structure and optical properties is probed with the combination of single crystal X-ray diffraction, optical diffuse reflectance spectroscopy and electronic structure calculations using the software package Wien2k. Finally, the current set of predictive tools employed to forcast diamond-like structures are reviewed, including the adherence of these guidelines to the novel compound Ag2FeSiS4 as well all over 60 ternary and quaternary diamond-like materials currently reported in the literature. Furthermore, the most common radii sets used for the prediction of bond distance and cell parameters in these materials are compared to the observed bond distances in quaternary diamond-like nonoxide materials. / Bayer School of Natural and Environmental Sciences / Chemistry and Biochemistry / PhD; / Dissertation;

A phenomenon as complex as protein folding requires a complex model to approximate it. This thesis presents a bottom-up approach for building complex probabilistic models of protein secondary structure by incorporating the multiple information sources which we call experts. Expert opinions are represented by probability distributions over the set of possible structures. Bayesian treatment of a group of experts results in a consensus opinion that combines the experts' probability distributions using the operators of normalized product, quotient and exponentiation. The expression of this consensus opinion simplifiesto a product of the expert opinions with two assumptions: (1) balanced training of experts, i. e. , uniform prior probability over all structures, and (2) conditional independence between expert opinions,given the structure. This research also studies how Markov chains and hidden Markov models may be used to represent expert opinion. Closure properties areproven, and construction algorithms are given for product of hidden Markov models, and product, quotient and exponentiation of Markovchains. Algorithms for extracting single-structure predictions from these models are also given. Current product-of-experts approaches in machine learning are top-down modeling strategies that assume expert independence, and require simultaneous training of all experts. This research describes a bottom-up modeling strategy that can incorporate conditionally dependent experts, and assumes separately trained experts.

Predicting protein structure using its primary sequence has always been a challenging topic in biochemistry. Although it seems as simple as finding the minimal energy conformation, it has been quite difficult to provide an accurate yet reliable solution for the problem. On the one hand, the lack of understanding of the hydrophobic effect as well as the relationship between different stabilizing forces, such as hydrophobic interaction, hydrogen bonding and electronic static interaction prevent the scientist from developing potential functions to estimate free energy. On the other hand, structure databases are limited with redundant structures, which represent a noncontinuous, sparsely-sampled conformational space, and preventing the development of a method suitable for high-resolution, high-accuracy structure prediction that can be applied for functional annotation of an unknown protein sequence. Thus, in this study, we use molecular dynamics simulation as a tool to sample conformational space. Structures were generated with physically realistic conformations that represented the properties of ensembles of native structures. First, we focused our study on the relationship among different factors that stabilize protein structure. Using a wellcharacterized mutation system of the B-hairpin, a fundamental building block of protein, we were able to identify the effect of terminal ion-pairs (salt-bridges) on the stability of the beta-hairpin, and its relationship with hydrophobic interactions and hydrogen bonds. In the same study, we also correlated our theoretical simulations qualitatively with experimental results. Such analysis provides us a better understanding of beta-hairpin stability and helps us to improve the protein engineering method to design more stable hairpins. Second, with large-scale simulations of different representative protein folds, we were able to conduct a fine-grained analysis by sampling the continuous conformational space to characterize the relationship among backbone conformation, side-chain conformation and side-chain packing. Such information is valuable for improving high-resolution structure prediction. Last, with this information, we developed a new prediction algorithm using packing information derived from the conserved relative packing groups. Based on its performance in CASP7, we were able to draw the conclusion that our simulated dataset as well as our packing-oriented prediction method are useful for template based structure prediction.

Bioinformatics applications can address the transfer of information at several stages of the central dogma of molecular biology, including transcription and translation. This dissertation focuses on using Bayesian models to interpret biological data in bioinformatics, using Markov chain Monte Carlo (MCMC) for the inference method. First, we use our approach to interpret data at the transcription level. We propose a two-level hierarchical Bayesian model for variable selection on cDNA Microarray data. cDNA Microarray quantifies mRNA levels of a gene simultaneously so has thousands of genes in one sample. By observing the expression patterns of genes under various treatment conditions, important clues about gene function can be obtained. We consider a multivariate Bayesian regression model and assign priors that favor sparseness in terms of number of variables (genes) used. We introduce the use of different priors to promote different degrees of sparseness using a unified two-level hierarchical Bayesian model. Second, we apply our method to a problem related to the translation level. We develop hidden Markov models to model linker/non-linker sequence regions in a protein sequence. We use a linker index to exploit differences in amino acid composition between regions from sequence information alone. A goal of protein structure prediction is to take an amino acid sequence (represented as a sequence of letters) and predict its tertiary structure. The identification of linker regions in a protein sequence is valuable in predicting the three-dimensional structure. Because of the complexities of both models encountered in practice, we employ the Markov chain Monte Carlo method (MCMC), particularly Gibbs sampling (Gelfand and Smith, 1990) for the inference of the parameter estimation.

Nowadays, RNA is extensively acknowledged an important role in the functions of information transfer, structural components, gene regulation and etc. The secondary structure of RNA becomes a key to understand structure-function relationship. Computational prediction of RNA secondary structure does not only provide possible structures, but also elucidates the mechanism of RNA folding. Conventional prediction programs are either derived from evolutionary perspective, or aimed to achieve minimum free energy. In vivo, RNA folds during transcription, which indicates that native RNA structure is a result from both thermodynamics and kinetics. In this thesis, I first reviewed the current leading kinetic folding programs and demonstrate that these programs are not able to predict secondary structure accurately. Upon that, I proposed a new sequential folding program called GTkinetics. Given an RNA sequence, GTkinetics predicts a secondary structure and a series of RNA folding trajectories. It treats the RNA as a growing chain, and adds stable local structures sequentially. It is featured with a Z-score to evaluate stability of local structures, which is able to locate native local structures with high confidence. Since all stable local structures are captured in GTkinetics, it results in some false positives, which prevents the native structure to form as the chain grows. This suggests a refolding model to melt the false positive hairpins, probable intermediate structures, and to fold the RNA into a new structure with reliable long-range helices. By analyzing suboptimal ensemble along the folding pathway, I suggested a refolding mechanism, with which refolding can be evaluated whether or not to take place. Another way to favor local structures over long-distance structures, we introduced a distance penalty function into the free energy calculation. I used a sigmoidal function to compute the energy penalty according to the distance in the primary sequence between two nucleotides of a base pair. For both the training dataset and the test dataset, the distance function improves the prediction to some extent. In order to characterize the differences between local and long-range helices, I carried out analysis of standardized local nucleotide composition and base pair composition according to the two groups. The results show that adenine accumulates on the 5' side of local structure, but not on that of long-range helices. GU base pairs occur significantly more frequent in the local helices than that in the long-range helices. These indicate that the mechanisms to form local and long range helices are different, which is encoded in the sequence itself. Based on all the results, I will draw conclusions and suggest future directions to enhance the current sequential folding program.

This thesis uses first principles techniques, mainly the ab initio random structure searching method (AIRSS), to study anode materials for lithium- and sodium- ion batteries (LIBs and NIBs, respectively). Initial work relates to a theoretical structure prediction study of the lithium and sodium phosphide systems in the context of phosphorus anodes as candidates for LIBs and NIBs. The work reveals new Li-P and Na-P phases, some of which can be used to better interpret previous experimental results. By combining AIRSS searches with a high-throughput screening search from structures in the Inorganic Crystal Structure Database (ICSD), regions in the phase diagram are correlated to different ionic motifs and NMR chemical shielding is predicted from first principles. An electronic structure analysis of the Li-P and Na-P compounds is performed and its implication on the anode performance is discussed. The study is concluded by exploring the addition of aluminium dopants to the Li-P compounds to improve the electronic conductivity of the system. The following work deals with a study of tin anodes for NIBs. The structure prediction study yields a variety of new phases; of particular interest is a new NaSn$_2$ phase predicted by AIRSS. This phase plays a crucial role in understanding the alloying mechanism of high-capacity tin anodes, work which was done in collaboration with experimental colleagues. Our predicted theoretical voltages give excellent agreement with the experimental electrochemical cycling curve. First principles molecular dynamics is used to propose an amorphous Na$_1$Sn$_1$ model which, in addition to the newly derived NaSn$_2$ phase, provides help in revealing the electrochemical processes. In the subsequent work, we study Li-Sn and Li-Sb intermetallics in the context of alloy anodes for LIBs. A rich phase diagram of Li-Sn is present, exhibiting a variety of new phases. The calculated voltages show excellent agreement with previously reported cycling measurements and a consistent structural evolution of Li-Sn phases as Li concentration increases is revealed. The study concluded by calculating NMR parameters on the hexagonal- and cubic-Li$_3$Sb phases which shed light on the interpretation of reported experimental data. We conclude with a structure prediction study of the pseudobinary Li-FeS$_2$ system, where FeS$_2$ is considered as a potential high-capacity electrochemical energy storage system. Our first principles calculations of intermediate structures help to elucidate the mechanism of charge storage observed by our experimental collaborators via $\textit{in operando}$ studies.

The secondary structure for nucleic acids provides a level of description that is both abstract enough to allow for efficient algorithms and realistic enough to provide a good approximate to the thermodynamic and kinetics properties of RNA structure formation. The secondary structure model has furthermore been successful in explaining salient features of RNA evolution in nature and in the test tube. In this contribution we review the computational chemistry of RNA secondary structures using a simplified algorithmic approach for explanation.