A Dissertation submitted to the Faculty of Science, University of Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. 2015. / Prdm1/Blimp1 is a transcription factor whose mechanism of action is mainly repression; however it has been identified as an activator in some cases. As a transcriptional repressor, it plays multiple roles during embryonic development, including neural crest specification. Prdm1 acts by repressing large sets of genes via sequence specific recruitment of co-repressors, many of which are epigenetic modifiers. Neural crest is a transient, migrating cell population that gives rise to a number of diverse cell lineages that form important structures in the vertebrate embryo. Examples of these include peripheral nervous system, melanocytes and cranial cartilage. Prdm1 is expressed during neural crest specification in Xenopus, zebrafish and lamprey. The expression of Prdm1 had not yet been investigated in the neural crest during chick embryonic development. The mechanism of Prdm1 action or the nature of possible binding partners that mediate its effects in the neural crest had not yet been addressed. Prdm1 binding partners are known to play important roles during embryonic development, yet in many cases no spatiotemporal expression analysis during early vertebrate development has been performed. Single and double in situ hybridization for Prdm1 and all the binding partners was performed to determine localization of mRNA during early stages of chick embryonic development. We report the expression patterns of Prdm1 and seven of its known or putative binding partners (Hdac1, Hdac2, Tle1, Tle3, G9a, Prmt5 and Lsd1) during early stages (HH4-HH10) of chicken embryogenesis. Prdm1 expression was observed in the neural plate border and pre-migratory neural crest during chick development. Six Prdm1 binding partners (except Tle1) are co- expressed with Prdm1 in the prospective neural plate border at HH4-HH6, and all seven show strong and specific expression in the neural plate border at HH7-HH8, suggesting all of them co-operate with Prdm1 during neural crest development in chick embryos. Future work will focus on protein interaction studies in order to directly demonstrate the association between Prdm1 and the binding partners it co-localizes with.

Aggregation of amyloid β (Aβ) protein has been linked to the development of Alzheimer's Disease (AD). The genesis of Aβ involves the cleavage Amyloid Precursor Protein (APP) by β-secretase, producing the 99-residue C99 peptide, and the subsequent cleavage of C99 by γ-secretase to produce Aβ. A detailed understanding of the γ-cleavage process is essential to our undertsanding of the pathological mechanisms linking the aggregation of Aβ to the development of AD. This work seeks to provide insight into critical aspects of the structure and dynamics of C99, and the particular roles played by (1) C99 amino acid sequence and (2) the lipid composition of the membrane environment. Many studies have focused on the importance of the C99 sequence, including known studies of Familial AD (FAD) mutants as well as engineered mutations. Specific mutations have been found to affect the processing of C99, which has been linked to changes in the structure of C99 and the formation of C99 homodimers. Similarly, changes in the membrane environment, through variation in lipid composition and the presence of cholesterol, have been found to affect C99 structure and positioning within the membrane as well as C99 dimerization. The results of this work extend our understanding of the APP-C99 system and its interaction with the environment. Using a multiscale simulation approach, we find key structural effects of engineered mutations that suggest possible mechanistic insight into the γ-cleavage process. Using C99 congener peptides, we examine the effect of local membrane environment on the dimerization of C99, focusing on the roles of both the transmembrane (TM) region as well as the juxtamembrane (JM) domain. Further studies characterize the role of a FAD mutation, and demonstrate the effect of the mutation on the dimerization of C99 in agreement with experimental findings. Overall, this work leads to critical insight into the role of sequence and membrane on the structure of C99 in a membrane environment, and provides support for the conjecture that the structure of C99 monomer and homodimer are critical to our understanding of the processing of C99, a critical step in the genesis of Aβ peptide and the etiology of Alzheimer's Disease.

Thesis advisor: Evan R. Kantrowitz / The regulatory mechanism of allostery is exhibited by certain proteins such as Escherichia coli aspartate transcarbamoylase (ATCase), and is defined as the change in shape and activity (of enzymes) resulting from the binding of particular molecules at locations distant from the active site. This particular enzyme and the property of allostery in general have been investigated for several decades, yet the molecular mechanisms underlying allosteric regulation remain unclear. Therefore in this thesis we have attempted via several biophysical methods, along with the tools of molecular biology and biochemistry, to correlate the changes in allosteric structure with presence of the allosteric effectors and enzymatic activity. We created a double mutant version of ATCase, in which the only native cysteine residue in the catalytic chain was mutated to alanine and another alanine on a loop was mutated to cysteine, in order to lock the enzyme into the R allosteric state by disulfide bonds. This disulfide locked R state exhibited no regulation by the allosteric effectors ATP and CTP and lost all cooperativity for aspartate, and then regained those regulatory properties after the disulfide links were severed by addition of a reducing agent. This double mutant was then chemically modified by covalent attachment of a fluorescent probe. The T and R allosteric states of this fluorophore-labeled enzyme had dramatically different fluorescence emission spectra, providing a highly sensitive tool for testing the effects of the allosteric effectors on the allosteric state. The changes in the fluorescence spectra, and hence quaternary structure, matched the changes in activity after addition of ATP or CTP. This fluorophore labeled enzyme was also encapsulated within a solgel, changing the time scale of the allosteric transition from milliseconds to several hours. The fluorophore labels allowed monitoring the allosteric state within the sol-gel, and the physically trapped T and R states both showed no regulation by the allosteric effectors ATP and CTP, and no cooperativity for aspartate. The trapped T state had low-affinity for aspartate and low activity, and the trapped R state had high-affinity for aspartate and high activity. Timeresolved small-angle x-ray scattering (TR-SAXS) was used to determine the kinetics of the allosteric transition, and to monitor the structure of the enzyme in real time after the addition of substrates and allosteric effectors. These TR-SAXS studies demonstrated a correlation between the presence of the allosteric effectors, the quaternary allosteric state, and activity, suggesting like the previous studies in this thesis that the behavior of ATCase is well explained by the twostate model. However, the effector ATP appeared to destabilize the T state and CTP to destabilize the R state, suggesting a different allosteric molecular mechanism than that of the two-state model. This thesis demonstrates the validity of many of the concepts of the two-state model, while suggesting minor modifications to that elegantly simple model in order to conform with the complex structure and function of ATCase. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

<p>Proteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence.</p><p>This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.</p>

Proteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence. This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.

Protein structure prediction is concerned with the prediction of a protein's three dimensional structure from its amino acid sequence. Such predictions are commonly performed by searching the possible structures and evaluating each structure by using some scoring function. If it is assumed that the target protein structure resembles the structure of a known protein, the search space can be significantly reduced. Such an approach is referred to as comparative structure prediction. When such an assumption is not made, the approach is known as ab initio structure prediction. There are several difficulties in devising efficient searches or in computing the scoring function. Many of these problems have ready solutions from known mathematical methods. However, the problems that are yet unsolved have hindered structure prediction methods from more ideal predictions. The objective of this study is to present a complete framework for ab initio protein structure prediction. To achieve this, a new search strategy is proposed, and better techniques are devised for computing the known scoring functions. Some of the remaining problems in protein structure prediction are revisited. Several of them are shown to be intractable. In many of these cases, approximation methods are suggested as alternative solutions. The primary issues addressed in this thesis are concerned with local structures prediction, structure assembly or sampling, side chain packing, model comparison, and structural alignment. For brevity, we do not elaborate on these problems here; a concise introduction is given in the first section of this thesis. Results from these studies prompted the development of several programs, forming a utility suite for ab initio protein structure prediction. Due to the general usefulness of these programs, some of them are released with open source licenses to benefit the community.

Modeling loops is an often necessary step in protein structure and function determination, even with experimental X-ray and NMR data. It is well known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with sub-angstrom accuracy when compared with ab initio techniques, but the disadvantage of often being able to produce usable results as they depend entirely on the loop already being represented within the database. My contribution is the LoopWeaver protocol, a database method that uses multidimensional scaling to rapidly achieve better clash-free, low energy placement of loops obtained from a database of protein structures. This maintains the above- mentioned advantage while avoiding the disadvantage by permitting the use of lower quality matches that would not otherwise fit. Test results show that this method achieves significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper before refinement. With refinement, the results (LoopWeaver and Loopy combined) are better than ROSETTA's, with 0.53Å RMSD on average for 206 loops of length 6, 0.75Å local RMSD for 168 loops of length 7, 0.93Å RMSD for 117 loops of length 8, and 1.13Å RMSD loops of length 9, while ROSETTA scores 0.66Å , 0.93Å , 1.23Å , 1.56Å , respectively, at the same average time limit (3 hours on a 2.2 GHz Opteron). When ROSETTA is allowed to run for over a week against LoopWeaver's and Loopy's combined 3 hours, it approaches, but does not surpass, this accuracy.

Protein structure prediction is concerned with the prediction of a protein's three dimensional structure from its amino acid sequence. Such predictions are commonly performed by searching the possible structures and evaluating each structure by using some scoring function. If it is assumed that the target protein structure resembles the structure of a known protein, the search space can be significantly reduced. Such an approach is referred to as comparative structure prediction. When such an assumption is not made, the approach is known as ab initio structure prediction. There are several difficulties in devising efficient searches or in computing the scoring function. Many of these problems have ready solutions from known mathematical methods. However, the problems that are yet unsolved have hindered structure prediction methods from more ideal predictions. The objective of this study is to present a complete framework for ab initio protein structure prediction. To achieve this, a new search strategy is proposed, and better techniques are devised for computing the known scoring functions. Some of the remaining problems in protein structure prediction are revisited. Several of them are shown to be intractable. In many of these cases, approximation methods are suggested as alternative solutions. The primary issues addressed in this thesis are concerned with local structures prediction, structure assembly or sampling, side chain packing, model comparison, and structural alignment. For brevity, we do not elaborate on these problems here; a concise introduction is given in the first section of this thesis. Results from these studies prompted the development of several programs, forming a utility suite for ab initio protein structure prediction. Due to the general usefulness of these programs, some of them are released with open source licenses to benefit the community.

Protein is a fundamental material of life. There are many kinds of proteins in the body. If one of them malfunctions, it will cause physical problems. Therefore, many scientists try to analyze the functions of proteins. It is believed that the protein structure determines its function. The more similar the structures are, the more similar their functions are. Therefore, the prediction and comparison of protein structures are important topics in bioinformatics. Typically, distance RMSD (Root Mean Square Deviation) is a method used by most scientists to measure the distance between two structures. In this thesis, we propose a new algorithm to compare two protein structures, which is based on the comparison of curves in the space. To test and verify our method, we randomly choose some families in the CATH database and try to identify them. Experimental results show that our method outperforms RMSD. Furthermore, we also use the SVM (Support Vector Machine) tool to help us to obtain the better classification.

The numbers of atoms in a protein molecule are large, from 103 to 104. If we try to solve the positions of all atoms in a protein molecule, we usually can¡¦t get the result due to tremendous degrees of freedom. Here, we use the uniform cylinders to replace the helixes found in most protein molecules, and reduce the degrees of freedom dramatically. We also adapt Su¡¦s method to avoid the ¡§X-ray phase problem¡¨. In this thesis, we simulate the small angle X-ray diffraction data of atoms which uniformly confined in cylinders, and then using trying cylinders to simulate the real cylinders. Our study shows that we can find the cylinders¡¦ positions quite successfully and efficiently. Our approach provides a promising way to find out the helix structure of proteins.