|Jacob D. Jaffe, Ph.D., is the Assistant Director of the Proteomics Platform at the Broad Institute. He obtained his B.A. in Biochemistry from the University of Pennsylvania and his Ph.D. from Harvard University where he studied with George Church and Howard Berg. Dr. Jaffe has pioneered diverse problems in modern proteomics including large-scale mapping of proteomic data onto genomes, thus allowing their de novo annotation from proteomic evidence, pattern recognition for quantitative proteomics, determination and quantification of epigenetic marks on histone proteins, and high-throughput targeted phosphoproteomics.
Discovery to Targets for a Phosphoproteomic Signature Assay: One-stop shopping in Skyline
The effective dimensionality of the phosphoproteome is actually much smaller than the number of phosphosites due to coordinate regulation by a limiting number of kinases and phosphatases. To study these properties, we generated a large perturbational phosphoproteomics data set across multiple cell types and compounds using SILAC methodologies. Initial analysis of the data suggests that there are indeed groups of coordinately regulated sites that could be condensed into clusters. Some response clusters were cell-line independent, and some clusters showed responses that clearly represent similar activities by structural analogs. This source dataset may be the largest perturbational phosphoproteomic dataset in existence.
We seek to collapse the phosphoproteome into a limited number of sites that can be monitored in a targeted MS-based assay. This assay will be used to generate data for the Library of Integrated Cellular Signatures (LINCS) consortium. We call this assay the P100 assay, as an homage to the L1000 assay which achieves dimensionality reduction for gene expression. We collapsed the data into 55 clusters, and picked two exemplary phosphopeptides from each for the configuration of the assay. We have synthesized these peptides, their non-phosphorylated cognates, and distal peptides from the same protein so that we may determine stoichiometry.
Skyline is our tool of choice for rapidly coöpting discovery data initially processed with MaxQuant into a framework for a targeted this assay, and we will distribute assay parameters and aggregate cross-laboratory results via Panorama.