Deanna L. Auberry

Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags

Understanding biological systems and the roles of their constituents is facilitated by the ability to make quantitative, sensitive, and comprehensive measurements of how their proteome changes, e.g., in response to environmental perturbations. To this end, we have developed a high-throughput methodology to characterize an organisms dynamic proteome based on the combination of global enzymatic digestion, high-resolution liquid chromatographic separations, and analysis by Fourier transform ion cyclotron resonance mass spectrometry. The peptides produced serve as accurate mass tags for the proteins and have been used to identify with high confidence >61% of the predicted proteome for the ionizing radiation-resistant bacterium Deinococcus radiodurans. This fraction represents the broadest proteome coverage for any organism to date and includes 715 proteins previously annotated as either hypothetical or conserved hypothetical.

Global Analysis of Deinococcus Radiodurans Proteome by Csing Accurate Mass Tags

Understanding biological systems and the roles of their constituents is facilitated by the ability to make quantitative, sensitive, and comprehensive measurements of how their proteome changes, e.g., in response to environmental perturbations. To this end, we have developed a high-throughput methodology to characterize an organisms dynamic proteome based on the combination of global enzymatic digestion, high-resolution liquid chromatographic separations, and analysis by Fourier transform ion cyclotron resonance mass spectrometry. The peptides produced serve as accurate mass tags for the proteins and have been used to identify with high confidence >61% of the predicted proteome for the ionizing radiation-resistant bacterium Deinococcus radiodurans. This fraction represents the broadest proteome coverage for any organism to date and includes 715 proteins previously annotated as either hypothetical or conserved hypothetical.

A Bayesian estimator of protein–protein association probabilities

UNLABELLED The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments. AVAILABILITY BEPro (3) is public domain software, has been tested on WIndows XP, Linux and Mac OS, and is freely available from http://www.pnl.gov/statistics/BEPro3. SUPPLEMENTARY INFORMATION A user guide, example dataset with analysis and additional documentation are included with the BEPro (3) download.

Characterization of Plasma Membrane Proteins from Ovarian Cancer Cells Using Mass Spectrometry

To determine how the repertoire of plasma membrane proteins change with disease state, specifically related to cancer, several methods for preparation of plasma membrane proteins were evaluated. Cultured cells derived from stage IV ovarian tumors were grown to 90% confluence and harvested in buffer containing CHAPS detergent. This preparation was centrifuged at low speed to remove insoluble cellular debris resulting in a crude homogenate. Glycosylated proteins in the crude homogenate were selectively enriched using lectin affinity chromatography. The crude homogenate and the lectin purified sample were prepared for mass spectrometric evaluation. The general procedure for protein identification began with trypsin digestion of protein fractions followed by separation by reversed phase liquid chromatography that was coupled directly to a conventional tandem mass spectrometer (i.e. LCQ ion trap). Mass and fragmentation data for the peptides were searched against a human proteome data base using the informatics program SEQUEST. Using this procedure 398 proteins were identified with high confidence, including receptors, membrane-associated ligands, proteases, phosphatases, as well as structural and adhesion proteins. Results indicate that lectin chromatography provides a select subset of proteins and that the number and quality of the identifications improve as does the confidence of the protein identifications for this subset. These results represent the first step in development of methods to separate and successfully identify plasma membrane proteins from advanced ovarian cancer cells. Further characterization of plasma membrane proteins will contribute to our understanding of the mechanisms underlying progression of this deadly disease and may lead to new targeted interventions as well as new biomarkers for diagnosis.

An analysis pipeline for the inference of protein-protein interaction networks

We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis.

SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction

The Software Environment for Biological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and testing of network inference algorithms that use high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein- protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. In this paper, we present a case study on the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein- protein interaction networks from sets of mass spectrometry bait-prey experiment data.This paper addresses the problem of understanding preservation and reconstruction requirements for computer- aided medical decision-making. With an increasing number of computer-aided decisions having a large impact on our society, the motivation for our work is not only to document these decision processes semi-automatically but also to understand the preservation cost and related computational requirements. Our objective is to support computer-assisted creation of medical records, to guarantee authenticity of records, as well as to allow managers of electronic medical records (EMR), archivists and other users to explore and evaluate computational costs (e.g., storage and processing time) depending on several key characteristics of appraised records. Our approach to this problem is based on designing an exploratory simulation framework for investigating preservation tradeoffs and assisting in appraisals of electronic records. We have a prototype simulation framework called image provenance to learn (IP2Learn) to support computer-aided medical decisions based on visual image inspection. The current software enables to explore some of the tradeoffs related to (1) information granularity (category and level of detail), (2) representation of provenance information, (3) compression, (4) encryption, (5) watermarking and steganography, (6) information gathering mechanism, and (7) final medical report content (level of detail) and its format. We illustrate the novelty of IP2Learn by performing example studies and the results of tradeoff analyses for a specific image inspection task.