To assess the similarities and the differences among protein structures, a variety of structure alignment algorithms and programs have been designed and implemented. We introduce a low-resolution approach and a high-resolution approach to evaluate the similarities among protein structures. Our results show that both the low-resolution approach and the high-resolution approach outperform state-of-the-art methods. For the low-resolution approach, we eliminate false positives through the comparison of both local similarity and remote similarity with little compromise in speed. Two kinds of contact libraries (ContactLib) are introduced to fingerprint protein structures effectively and efficiently. Each contact group from the contact library consists of one local or two remote fragments and is represented by a concise vector. These vectors are then indexed and used to calculate a new combined hit-rate score to identify similar protein structures effectively and efficiently. We tested our ContactLibs on the high-quality protein structure subset of SCOP30, which contains 3,297 protein structures. For each protein structure of the subset, we retrieved its neighbor protein structures from the rest of the subset. The best area under the ROC curve, archived by a ContactLib, is as high as 0.960. This is a significant improvement over 0.747, the best result achieved by the state-of-the-art method, FragBag. For the high-resolution approach, our PROtein STructure Alignment method (PROSTA) relies on and verifies the fact that the optimal protein structure alignment always contains a small subset of aligned residue pairs, called a seed, such that the rotation and translation (ROTRAN), which minimizes the RMSD of the seed, yields both the optimal ROTRAN and the optimal alignment score. Thus, ROTRANs minimizing the RMSDs of small subsets of residues are sampled, and global alignments are calculated directly from the sampled ROTRANs. Moreover, our method incorporates remote information and filters similar ROTRANs (or alignments) by clustering, rather than by an exhaustive method, to overcome the computational inefficiency. Our high-resolution protein structure alignment method, when applied to optimizing the TM-score and the GDT-TS score, produces a significantly better result than state-of-the-art protein structure alignment methods. Specifically, if the highest TM-score found by TM-align is lower than 0.6 and the highest TM-score found by one of the tested methods is higher than 0.5, our alignment method tends to discover better protein structure alignments with (up to 0.21) higher TM-scores. In such cases, TM-align fails to find TM-scores higher than 0.5 with a probability of 42%; however, our alignment method fails the same task with a probability of only 2%. In addition, existing protein structure alignment scoring functions focus on atom coordinate similarity alone and simply ignore other important similarities, such as sequence similarity. Our scoring function has the capacity for incorporating multiple similarities into the scoring function. Our result shows that sequence similarity aids in finding high quality protein structure alignments that are more consistent with HOMSTRAD alignments, which are protein structure alignments examined by human experts. When atom coordinate similarity itself fails to find alignments with any consistency to HOMSTRAD alignments, our scoring function remains capable of finding alignments highly similar to, or even identical to, HOMSTRAD alignments.

Understanding normal and cancer cell biology requires the development and application of systems biology approaches capable of probing the functional human proteome, and the protein-protein interactions (PPIs) within it. Such technologies will facilitate our understanding of how molecular events drive phenotypic outcomes, and how these processes are perturbed in disease conditions. In this thesis, I first describe the development of a mammalian, Gateway compatible, lentivirus-based protein-fragment complementation assay (magical-PCA), for the in vivo high-throughput identification of PPIs in mammalian cells. This technology provides a vast improvement over current PCA methodologies by allowing for pooled, proteome-scale mapping of PPIs in any mammalian cell line of interest, using any bait protein of interest. A proof-of-concept pooled genome-scale screen using the magical-PCA approach was performed using the human mitochondrial protein TOMM22 as a bait, providing evidence that this technology is amenable to proteome-wide screens. Moreover, the TOMM22 screens offered novel insight into links between TOMM22 and proteins involved in mitochondrial organization, apoptosis, and cell cycle dynamics. Second, I performed a pooled genome-scale magical-PCA screen with the oncoprotein BMI1, a component of the E3 ubiquitin ligase complex involved in histone H2A mono-ubiquitination and gene silencing, to identify novel BMI1 protein interactors. Consequently, I have uncovered a novel physical and functional association between BMI1 and components of the mammalian splicing machinery. I further discovered that BMI1 knockdown influenced the alternative splicing of a number of cellular pre-mRNAs in colon cancer cell lines, suggesting that the association between BMI1 and cellular splicing factors impinges on pre-mRNA processing. Importantly, BMI1 expression was shown to influence the alternative splicing of the SS18 oncoprotein towards an exon 8-excluded isoform, which was shown in this study to promote cell proliferation when assessed in an anchorage-independent growth assay. Together, these studies highlight the development of a new methodology for the detection and proteome-scale screening of mammalian PPIs. A proof-of-concept screen with human TOMM22 highlighted the utility of the approach, as I was able to detect both strong and weak or transient PPIs. Application of my screening methodology to BMI1 provided crucial insight into the function of this oncoprotein, and BMI1-driven tumorigenesis.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 162-188). / Also available by subscription via World Wide Web / xvi, 188 leaves, bound ill. (some col.) 29 cm

Termination of protein synthesis occurs in response to the translocation of a stop codon (UAA, UAG or UGA) into the A site of the ribosome. Unlike sense codons, stop signals in the mRNA are recognized by two classes of specialized proteins called release factors (RFs): the class I or decoding RF, which recognizes the stop codon and promotes peptidyl-tRNA hydrolysis and class II RF, a G-protein that promotes the dissociation of the decoding RF from the ribosome. The discovery that stop codons are decoded by a protein factor rather than a specific tRNA opened up the possibility that the signal for termination of protein synthesis might extend beyond the stop codon itself. Biochemical and genetic experiments in prokaryotes confirmed that bias in nucleotide usage around stop codons correlates with translation termination efficiency. The objective of the current investigation was to define the eukaryotic termination signal by determining the bias in the nucleotide sequence surrounding eukaryotic stop codons and to identify whether this was a determinant of translation termination efficiency. Bioinformatic analysis of five diverse eukaryotic genomes was undertaken to identify potential eukaryotic translation termination signal elements. Significant nucleotide bias was identified both 5� and 3� of the stop codon in all the genomes investigated. Correlations were identified between nucleotide bias and gene expression levels, and between nucleotide bias and natural recoding sites predicting that nucleotides 5� and 3� of the stop codon affect termination efficiency. These correlations were common to all organisms investigated and suggested the existence of a eukaryotic termination signal. Termination signals identified from the bioinformatic analysis were assayed to determine the efficiency of termination in an in vitro dual luciferase reporter assay. Results indicated that nucleotides both 5� and 3� of the stop codon could significantly alter termination signal efficiency, although readthrough did not vary by greater than 1%. The effect of nucleotides 3� to the stop codon on termination efficiency was investigated further in mammalian cultured cells using the dual luciferase reporter assay. Results showed a significant relationship between the identity of these nucleotides and observed termination efficiencies with nucleotides at positions +4 and +8 giving the strongest correlation. Termination sequence elements of the form UGA CUN NCN mediated up to 5% readthrough in cultured cells. Investigations into the underlying mechanisms that were responsible for the variation in termination efficiency were also undertaken. Co-transfection of specific suppressor tRNAs enhanced but did not change the pattern of observed termination efficiency, indicating that the mechanisms mediated by the termination signal element was not mediated through suppressor tRNA binding. Alignments of 18S rRNA sequences indicated potential extensive interactions between the rRNA and the mRNA termination signal element. Experiments that assessed the effect of eRF1 levels on termination at inefficient termination signals in vitro revealed that increased levels of eRF1 could improve termination efficiency. These results indicate that, as in prokaryotes, specific nucleotides beyond the stop codon modulate translation termination efficiency in eukaryotes, and that the translation termination signal should be considered a sequence element.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 87-94).

Thesis (Ph. D.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains xii, 156 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references.

Thesis (Ph. D.)--University of Oregon, 2007. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 67-80). Also available for download via the World Wide Web; free to University of Oregon users.

Techn. University, Diss., 2006--München.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 162-188).

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 114-125) Also available in print.