Quantitative protein network monitoring in response to DNA damage.

Conventional molecular biology techniques have identified a large number of cell signaling pathways; however, the importance of these pathways often varies, depending on factors such as treatment type, dose, time after treatment, and cell type. Here, we describe a technique using "reverse-phase" protein lysate microarrays (RPAs) to acquire multiple dimensions of information on protein dynamics in response to DNA damage. Whole-cell lysates from three cellular stress treatments (IR, UV, and ADR) were collected at four doses per treatment, and each, in turn, at 10 time points, resulting in a single-slide RPA consisting of 10,240 features, including replicates. The dynamic molecular profile of 18 unique protein species was compared to phenotypic fate by FACS analysis for corresponding stress conditions. Our initial quantitative results in this new platform confirmed that (1) there is clear stress dose-response effect in p53 protein and (2) a comparison of the rates of increase of p21 and Cyclin D3/p53-Ser15 in response to DNA damage may be associated with the pattern of DNA content. This method, offering a quantitative time-course monitoring of protein expression levels, can provide an experimental reference for developing mathematical models of cell signaling dynamics. Although the present study focuses on the DNA damage-repair pathway, the technique is generally useful to the study of protein signaling.

Antibody screening database for protein kinetic modeling.

Knowledge-based proteomic studies rely on the availability of quality antibodies. The increasing number of commercially available antibodies covers a wide range of protein networks; however, performance of each antibody can vary, depending on what type of cells, treatments, and time points are studied. Here, we describe an antibody database in which we screened 279 antibodies against multiple cell lysates after various treatments and from different time points. We applied these quality-confirmed antibodies on protein arrays, showing their utility for protein kinetic modeling.

AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies.

BACKGROUND: Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java. DESCRIPTION: AbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin. CONCLUSION: AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at http://discover.nci.nih.gov/abminer.