Expediting SRM assay development for large-scale targeted proteomics experiments

Wu, Chaochao; Shi, Tujin; Brown, Joseph N.; He, Jintang; Gao, Yuqian; Fillmore, Thomas L.; Shukla, Anil K.; Moore, Ronald J.; Camp, David G.; Rodland, Karin D.; Qian, Weijun; Liu, Tao; Smith, Richard D.

doi:10.1021/pr500500d

Title: Expediting SRM assay development for large-scale targeted proteomics experiments

Journal Article · Fri Aug 22 00:00:00 EDT 2014 · Journal of Proteome Research

DOI:https://doi.org/10.1021/pr500500d· OSTI ID:1167647

Wu, Chaochao ^[1]; Shi, Tujin ^[1]; Brown, Joseph N. ^[1]; He, Jintang ^[1]; Gao, Yuqian ^[1]; Fillmore, Thomas L. ^[2]; Shukla, Anil K. ^[1]; Moore, Ronald J. ^[1]; Camp, David G. ^[1]; Rodland, Karin D. ^[1]; Qian, Weijun ^[1]; Liu, Tao ^[1]; Smith, Richard D. ^[1]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Biological Sciences Division
Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)

Due to their high sensitivity and specificity, targeted proteomics measurements, e.g. selected reaction monitoring (SRM), are becoming increasingly popular for biological and translational applications. Selection of optimal transitions and optimization of collision energy (CE) are important assay development steps for achieving sensitive detection and accurate quantification; however, these steps can be labor-intensive, especially for large-scale applications. Herein, we explored several options for accelerating SRM assay development evaluated in the context of a relatively large set of 215 synthetic peptide targets. We first showed that HCD fragmentation is very similar to CID in triple quadrupole (QQQ) instrumentation, and by selection of top six y fragment ions from HCD spectra, >86% of top transitions optimized from direct infusion on QQQ instrument are covered. We also demonstrated that the CE calculated by existing prediction tools was less accurate for +3 precursors, and a significant increase in intensity for transitions could be obtained using a new CE prediction equation constructed from the present experimental data. Overall, our study illustrates the feasibility of expediting the development of larger numbers of high-sensitivity SRM assays through automation of transitions selection and accurate prediction of optimal CE to improve both SRM throughput and measurement quality.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-76RL01830; U24-CA-160019; P41GM103493; DP2OD006668

OSTI ID:: 1167647

Report Number(s):: PNNL-SA-102906; 40991; 46206; 48135; 400412000

Journal Information:: Journal of Proteome Research, Vol. 13, Issue 10; ISSN 1535-3893

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 20 works

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CE prediction
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transition selection
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