Limit-of-Detection Proteomics Accelerates Clinical PTM Discovery

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Clinical discovery proteomics requires analyzing low abundance modified peptides (LAMPs).  We present a novel method down to limit-of-detection (LOD). Under ideal conditions, 12 raw ions (4 MS1, 8+ MS2) can identify/quantify a protein with localized PTMs.

Proteomics data typically contains more peptide ions (MS1) than fragment ions (MS2). One-third of MS1 peptides can be near-LOD with only 2 isotopic ions — still enough to calculate accurate mass and gross quantitation (apex intensity).  In other words, MS1 is a treasure trove of LAMPs with solid mass and quant info, but is largely untapped because of uncertain identity.

We show it’s possible to infer anonymous identity by triangulating MS1 masses to MS2 IDs. For example, in a phospho-peptide dataset, we can infer the sequence of a LOD peptide if its mass is exactly one phosphorylation less than an identified phospho-peptide.

For MS2 LOD analysis, a sequence of 8+ is often protein-unique. This means matching enough near-contiguous +1 y-ions can identify the protein plus in-range PTMs without statistics.

Many research projects involve PTMs of one known protein. Labs use trial-and-error DIA to select presumed modified peptides for MS2 identification. That is basically shooting in the dark and won’t work for LAMPs, so it’s possible to be empty-handed after months of experimentation.

A better way is to mine MS1 data to build a 2D map {(mass, time, quant)} of MS1 peptides of interest, for example those exactly one PTM mass different from MS2 peptide IDs (DIA or DDA). The map can either answer specific questions or optimize DIA experimentation from which another map is derived.

By letting researchers see the smallest details from the latest data, LOD analysis accelerates research while reducing risk and cost.