The ability to faithfully replicate DNA is dependent upon the maintenance and regulation of its precursors, the deoxyribonucleoside triphosphates. Enzymes encoded by the bacteriophage T4 have been widely used as models of biochemical processes. A body of evidence supports the concept that the bacteriophage T4 enzymes involved in deoxyribonucleotide biosynthesis are associated as a complex within the infected Escherichia coli. This dissertation describes the continued examination of the protein-protein interactions involved in deoxynucleotide biosynthesis of bacteriophage T4. My studies on the protein-protein interactions involved in deoxyribonucleotide biosynthesis focused on two unique phage proteins, the dCMP hydroxymethylase enzyme and the translational regulator RegA. An initial study was undertaken to determine if the generation of anti-idiotypic antibodies would prove useful in the identification of direct interactions between dCMP hydroxymethylase and other proteins of the deoxyribonucleotide synthetase complex. For the initial generation of anti-idiotypic antibodies, polyclonal rabbit antibodies were generated to affinity purified anti-dCMP hydroxymethylase polyclonal rabbit IgG. The anti-anti-dCMP hydroxymethylase antibody was found to be specific in binding to the bacteriophage T4 dTMP synthase. A second method to generate anti-idiotypic antibodies was attempted with the translational regulator RegA. The generation of anti-idiotypic antibodies to the RegA protein involved the purification of anti-RegA rabbit Fab fragments and the generation of anti-anti-RegA polyclonal antibodies within mice. This alternative method was determined to be inferior to the initial method for generating anti-idiotypic antibodies. Additional studies involved the examination of RegA protein-protein interactions using affinity chromatography. A number of bacteriophage T4 early proteins were determined to associate with an immobilized RegA column. / Graduation date: 1992

Graduation date: 1993

Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction.