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Expediting SRM Assay Development for Large-Scale Targeted Proteomics Experiments
Author(s) -
Chaochao Wu,
Tujin Shi,
Joseph N. Brown,
Jintang He,
Yuqian Gao,
Thomas Fillmore,
Anil Shukla,
Ronald J. Moore,
David Camp,
Karin Rodland,
Weijun Qian,
Tao Liu,
Richard Smith
Publication year - 2014
Publication title -
journal of proteome research
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/pr500500d
Subject(s) - expediting , context (archaeology) , proteomics , computer science , sensitivity (control systems) , instrumentation (computer programming) , chemistry , engineering , systems engineering , biology , electronic engineering , paleontology , biochemistry , gene , operating system
Because of its high sensitivity and specificity, selected reaction monitoring (SRM)-based targeted proteomics has become 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 that of CID in triple quadrupole (QQQ) instrumentation and that by selection of the top 6 y fragment ions from HCD spectra, >86% of the top transitions optimized from direct infusion with QQQ instrumentation are covered. We also demonstrated that the CE calculated by existing prediction tools was less accurate for 3+ precursors and that 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 illustrated the feasibility of expediting the development of larger numbers of high-sensitivity SRM assays through automation of transition selection and accurate prediction of optimal CE to improve both SRM throughput and measurement quality.

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