Andres B. Stergachis   Andrew B. Stergachisgraduated in 2007 from the University of Chicago with a B.S in Biological Chemistry and a B.A. in Chemistry. He is currently enrolled in the Medical Scientist Training Program (MSTP) at the University of Washington, pursuing his PhD in the laboratory of Dr. John Stamatoyannopoulos in the Department of Genome Sciences. His current studies focus on developing and applying novel proteomic and genomic approaches to better understand how different genes read and regulate the genome in a cell-type specific manner. These efforts include the use of targeted proteomics and ChIP-seq to better understand the nuclear abundance and distribution of human transcription factors.

Rapid empirical identification of optimal peptides for targeted proteomics

Targeted proteomics is a powerful approach that enables quantitative analysis of tryptic peptides from complex biological samples with high sensitivity and specificity. However, a major bottleneck limiting wider application of targeted proteomics has been the identification of optimal proteotypic peptides that are readily detectable by the mass spectrometer, as well as the characteristic fragmentation patterns of these peptides.

Here we report an empirically-driven approach for generating both optimal proteotypic peptides and their fragmentation patterns in a scalable, economical, and generalizable fashion.  To accomplish this, we leveraged the rich collection of tagged cDNA clones that are currently available for most human and model organism proteins. Using these clones, we generated in vitro-synthesized full-length protein samples, followed by tryptic digestion and mass spectrometry analysis using selected reaction monitoring (SRM) and the software package Skyline.

On average we were able to identify and rank eight proteotypic peptides per protein. Additionally, we empirically derived proteotypic peptides for 98% of the target proteins. Peptides we identified that were also present in the National Institute of Standards and Technology (NIST) spectral database, all had high spectral similarity scores, with 93% having dot-products greater than 0.85. Monitoring all 12 of the empirically identified proteotypic peptides from the transcription factor CTCF in trypsin-digested nuclear lysate from erythrolukemia cells (K562) revealed that the relative intensity of these peptides in vitro and in vivo closely matched. Furthermore, these empirically derived peptides enabled the robust quantification of transcription factors across diverse human cell types.

In summary, we demonstrate and validate a rapid and cost-efficient method for empirical identification of optimal proteotypic peptides and their fragmentation patterns using in vitro-synthesized proteins.  Our method can be readily applied to generate assays to identify and quantify structurally diverse low-abundance proteins, such as human transcription factors, in unfractionated cellular extracts.

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