Thesis (doctoral)--Lund University, 1997. / Added t.p. with thesis statement inserted.

Microvitellogenin is a female specific yolk protein from the tobacco hornworm moth Manduca sexta. A cDNA library was constructed from poly (A)⁺ RNA isolated from adult female fat body. cDNA clones of mRNA for microvitellogenin were isolated by screening the cDNA library with antiserum against microvitellogenin. The results of Northern blot analysis and hybrid selection indicated that the cDNA clone was specific for microvitellogenin. The complete nucleotide sequence of the 834 base pair cDNA insert has been determined by the dideoxy chain termination method. The deduced amino acid sequence was compared with the N-terminal sequence determined by Edman degradation, an amino terminal extension of 17 amino acids appeared to be a signal peptide. The cDNA sequence predicts that the mature microvitellogenin is a protein of 232 amino acids with a calculated molecular weight of 26,201. A comparison of the translated amino acid sequence with the sequences in National Biomedical Research Foundation protein library did not establish any sequence similarity with known proteins. The microvitellogenin gene begins to be expressed in the fat body on the first day of the wandering (prepupal) females as determined by using the cDNA insert as a probe to hybridize with the mRNA for microvitellogenin. The cDNA probe was also used to screen a genomic library of M. sexta, yielding three genomic clones for microvitellogenin. One of them was characterized and it contained the complete microvitellogenin gene. The gene sequence was determined. Comparison to the cDNA sequence showed that the microvitellogenin gene contains an intron near the 5'-end of the non-coding region. The 5'-flanking sequence of the gene has been compared to the same regions of yp genes of Drosophila and vitellogenin genes of locust, some similar sequences have been observed and discussed.

The ribosome is responsible for mRNA-templated protein translation in all living cells. The translational machinery (TM) has evolved to use 20 amino acids each esterified onto one of several tRNA bodies. While the active site of the ribosome, known as the peptidyl transferase center (PTC), is able to handle a remarkable amount of substrate diversity, many classes of unnatural amino acids are not compatible with the TM. For example, in the field of unnatural amino acid mutagenesis, the site-specific incorporation of biologically useful amino acids into proteins, such as fluorophores, has often proven to be unfeasible. This runs counter to the accepted notion that the ribosome is blind to the structure of the amino acid and is capable of accepting any amino acid as long as the mRNA codon: tRNA anticodon pairing is correct. Two studies by our group set out to test the hypothesis that the ribosome can indeed discriminate the structure of the amino acid. Using a fully purified E. coli translation system, the first study showed that natural amino acids misacylated onto fully modified but non-native tRNAs show small but reproducible effects on the steps of aminoacyl-tRNA (aa-tRNA) selection. The second study, in which I participated, utilized D-aa-tRNAs in the same E. coli translation system to study how amino acids of the inverted stereochemistry to those found in ribosomally-synthesized proteins affect translation elongation. We showed that these unnatural substrates serve as peptidyl acceptors but once translocated into the P-site of the ribosome, fail as peptidyl donors and stall translation elongation by inactivating the PTC. The motivation of my work has been to further characterize the effects of D-aa-tRNAs on translation elongation. To this end, I examined how the PTC is affected structurally and functionally by the presence of ribosomal substrates containing D-amino acids. Chapter one contains an introduction to this work. Chapter two describes chemical probing experiments that demonstrate that the presence of peptidyl-D-aminoacyl-tRNAs in the P-site of the ribosome allosterically modulates the secondary structure of ribosomal exit tunnel nucleotides A2058 and A2059. Chapter three describes how the reactivity of peptidyl-D-aminoacyl-tRNAs to form tripeptides is highly dependent on the identity of the amino acid it is reacting with; protein yields can be close to what is obtained with natural amino acids or almost completely abolished. Chapter four contains the methods used to do this research. From the observations presented here as well as from the work of other laboratories, a picture of the PTC emerges in which the pairing of the A- and P- site substrates is integral in either promoting or suppressing catalysis by the PTC. This work has implications for the field of unnatural amino acid mutagenesis, particularly for strategies to improve the incorporation of interesting unnatural amino acid by the ribosome. In addition, this work adds an important aspect to the growing body of knowledge of ribosome stalling at the PTC.

Hu, Xihao. / Thesis Ph.D. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 147-161). / Abstracts also in Chinese. / Title from PDF title page (viewed on 26, October, 2016). / Hu, Xihao.

Proteins are the basic building blocks of biological organisms, and are responsible for a variety of functions within them. Proteins are composed of unique amino acid sequences. Some has only one sequence, while others contain several sequences that are combined together. These combined amino acid sequences fold to form a unique three-dimensional (3D) shape. Although the sequences may fold proteins into different 3D shapes in diverse environments, proteins with similar amino acid sequences typically have similar 3D shapes and functions. Knowledge of the 3D shape of a protein is important in both protein function analysis and drug design, for example when assessing the toxicity reduction associated with a given drug. Due to the complexity of protein 3D shapes and the close relationship between shapes and functions, the prediction of protein 3D shapes has become an important topic in bioinformatics. This research introduces a new approach to predict proteins’ 3D shapes, utilizing a multilayer artificial neural network. Our novel solution allows one to learn and predict the representations of the 3D shape associated with a protein by starting directly from its amino acid sequence descriptors. The input of the artificial neural network is a set of amino acid sequence descriptors we created based on a set of probability density functions. In our algorithm, the probability density functions are calculated by the correlation between the constituent amino acids, according to the substitution matrix. The output layer of the network is formed by 3D shape descriptors provided by an information retrieval system, called CAPRI. This system contains the pose invariant 3D shape descriptors, and retrieves proteins having the closest structures. The network is trained by proteins with known amino acid sequences and 3D shapes. Once the network has been trained, it is able to predict the 3D shape descriptors of the query protein. Based on the predicted 3D shape descriptors, the CAPRI system allows the retrieval of known proteins with 3D shapes closest to the query protein. These retrieved proteins may be verified as to whether they are in the same family as the query protein, since proteins in the same family generally have similar 3D shapes. The search for similar 3D shapes is done against a database of more than 45,000 known proteins. We present the results when evaluating our approach against a number of protein families of various sizes. Further, we consider a number of different neural network architectures and optimization algorithms. When the neural network is trained with proteins that are from large families where the proteins in the same family have similar amino acid sequences, the accuracy for finding proteins from the same family is 100%. When we employ proteins whose family members have dissimilar amino acid sequences, or those from a small protein family, in which case, neural networks with one hidden layer produce more promising results than networks with two hidden layers, and the performance may be improved by increasing the number of hidden nodes when the networks have one hidden layer.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Proteins are the basic building blocks of biological organisms, and are responsible for a variety of functions within them. Proteins are composed of unique amino acid sequences. Some has only one sequence, while others contain several sequences that are combined together. These combined amino acid sequences fold to form a unique three-dimensional (3D) shape. Although the sequences may fold proteins into different 3D shapes in diverse environments, proteins with similar amino acid sequences typically have similar 3D shapes and functions. Knowledge of the 3D shape of a protein is important in both protein function analysis and drug design, for example when assessing the toxicity reduction associated with a given drug. Due to the complexity of protein 3D shapes and the close relationship between shapes and functions, the prediction of protein 3D shapes has become an important topic in bioinformatics. This research introduces a new approach to predict proteins’ 3D shapes, utilizing a multilayer artificial neural network. Our novel solution allows one to learn and predict the representations of the 3D shape associated with a protein by starting directly from its amino acid sequence descriptors. The input of the artificial neural network is a set of amino acid sequence descriptors we created based on a set of probability density functions. In our algorithm, the probability density functions are calculated by the correlation between the constituent amino acids, according to the substitution matrix. The output layer of the network is formed by 3D shape descriptors provided by an information retrieval system, called CAPRI. This system contains the pose invariant 3D shape descriptors, and retrieves proteins having the closest structures. The network is trained by proteins with known amino acid sequences and 3D shapes. Once the network has been trained, it is able to predict the 3D shape descriptors of the query protein. Based on the predicted 3D shape descriptors, the CAPRI system allows the retrieval of known proteins with 3D shapes closest to the query protein. These retrieved proteins may be verified as to whether they are in the same family as the query protein, since proteins in the same family generally have similar 3D shapes. The search for similar 3D shapes is done against a database of more than 45,000 known proteins. We present the results when evaluating our approach against a number of protein families of various sizes. Further, we consider a number of different neural network architectures and optimization algorithms. When the neural network is trained with proteins that are from large families where the proteins in the same family have similar amino acid sequences, the accuracy for finding proteins from the same family is 100%. When we employ proteins whose family members have dissimilar amino acid sequences, or those from a small protein family, in which case, neural networks with one hidden layer produce more promising results than networks with two hidden layers, and the performance may be improved by increasing the number of hidden nodes when the networks have one hidden layer.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 5 uppsatser.