bioRxivBiochemistry
Picky: a simple method designer for targeted proteomics
Abstract
Targeted proteomic approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) are increasingly popular because they enable sensitive and rapid analysis of a preselected set of proteins1-3. However, developing targeted assays is tedious and requires the selection, synthesis and mass spectrometric analysis of candidate peptides before the actual samples can be analyzed. The SRMatlas and ProteomeTools projects recently published fragmentation spectra of synthetic peptides covering the entire human proteome4,5. These datasets provide very valuable resources. However, extracting the relevant data for selected proteins of interest is not straightforward. For example, developing scheduled acquisition methods (i.e. analyzing specific peptides in defined elution time windows) is complicated and requires adjustments to specific chromatographic conditions employed. Moreover, the number of peptide candidates to be targeted in parallel often exceeds the analytical abilities of the mass spectrometer. In this case, the key question is which peptides can be omitted without losing too much information. Ideally, a method design tool would automatically select the most informative peptides in each retention time window. Until now, none of the available tools automatically generates such optimized scheduled SRM and PRM methods (Figure S1).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC=\"FIGDIR/small/163154_figS1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@828org.highwire.dtl.DTLVardef@17a73aorg.highwire.dtl.DTLVardef@e40740org.highwire.dtl.DTLVardef@9816e7_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. S1:C_FLOATNO Comparison between different available SRM or PRM method generators.\n\nC_FIG
- organism: human
- assay: proteomics
- research focus: software / tool
- research focus: clustering
- research focus: dimensionality reduction
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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