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Archaeological Proteomics: Method Development and Analysis of Protein-Ceramic Binding

Description: The analysis of protein residues recovered from archaeological artifacts provides a unique opportunity to reveal new information about past societies. However, many scientists are currently unwilling to accept protein-based results due to problems in method development and a basic lack of agreement regarding the ability of proteins to bind to, and preserve within, artifacts such as pottery. In this paper, I address these challenges by conducting a two-phase experiment. First, I quantitatively evaluate the tendency of proteins to sorb to ceramic matrices by using total organic carbon analysis and spectrophotometric assays to analyze samples of experimentally cooked ceramic. I then test a series of solvent and physical parameters in order to develop an optimized method for extracting and preparing protein residues for identification via mass spectrometry. Results demonstrate that protein strongly sorbs to ceramic and is not easily removed, despite repeated washing, unless an appropriate extraction strategy is used. This has implications for the future of paleodietary, conservation ecology and forensic research in that it suggests the potential for recovery of aged or even ancient proteins from ceramic matrices.
Date: May 2010
Creator: Barker, Andrew L.
Partner: UNT Libraries

Developing algorithms for predicting protein-protein interactions of homology modeled proteins.

Description: The goal of this project was to examine the protein-protein docking problem, especially as it relates to homology-based structures, identify the key bottlenecks in current software tools, and evaluate and prototype new algorithms that may be developed to improve these bottlenecks. This report describes the current challenges in the protein-protein docking problem: correctly predicting the binding site for the protein-protein interaction and correctly placing the sidechains. Two different and complementary approaches are taken that can help with the protein-protein docking problem. The first approach is to predict interaction sites prior to docking, and uses bioinformatics studies of protein-protein interactions to predict theses interaction site. The second approach is to improve validation of predicted complexes after docking, and uses an improved scoring function for evaluating proposed docked poses, incorporating a solvation term. This scoring function demonstrates significant improvement over current state-of-the art functions. Initial studies on both these approaches are promising, and argue for full development of these algorithms.
Date: January 1, 2006
Creator: Martin, Shawn Bryan; Sale, Kenneth L.; Faulon, Jean-Loup Michel & Roe, Diana C.
Partner: UNT Libraries Government Documents Department

Elicitation of Protein-Protein Interactions from Biomedical Literature Using Association Rule Discovery

Description: 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.
Date: August 2010
Creator: Samuel, Jarvie John
Partner: UNT Libraries

In vivo collection of rare proteins using kinesin-based "nano-harvesters".

Description: In this project, we have developed a novel platform for capturing, transport, and separating target analytes using the work harnessed from biomolecular transport systems. Nanoharvesters were constructed by co-organizing kinesin motor proteins and antibodies on a nanocrystal quantum dot (nQD) scaffold. Attachment of kinesin and antibodies to the nQD was achieved through biotin-streptavidin non-covalent bonds. Assembly of the nanoharvesters was characterized using a modified enzyme-linked immunosorbent assay (ELISA) that confirmed attachment of both proteins. Nanoharvesters selective against tumor necrosis factor-{alpha} (TNF-{alpha}) and nuclear transcription factor-{kappa}B (NF-{kappa}B) were capable of detecting target antigens at <100 ng/mL in ELISAs. A motility-based assay was subsequently developed using an antibody-sandwich approach in which the target antigen (TNF-{alpha}) formed a sandwich with the red-emitting nanoharvester and green-emitting detection nQD. In this format, successful sandwich formation resulted in a yellow emission associated with surface-bound microtubules. Step-wise analysis of sandwich formation suggested that the motility function of the kinesin motors was not adversely affected by either antigen capture or the subsequent binding of the detection nQDs. TNF-{alpha} was detected as low as {approx}1.5 ng/mL TNF-{alpha}, with 5.2% of the nanoharvesters successfully capturing the target analyte and detection nQDs. Overall, these results demonstrate the ability to capture target protein analytes in vitro using the kinesin-based nanoharvesters in nanofluidic environments. This system has direct relevance for lab-on-a-chip applications where pressure-driven or electrokinetic movement of fluids is impractical, and offers potential application for in vivo capture of rare proteins within the cytoplasmic domain of live cells.
Date: November 1, 2008
Creator: Bachand, Marlene; Bachand, George David; Greene, Adrienne Celeste & Carroll-Portillo, Amanda
Partner: UNT Libraries Government Documents Department