TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets

peer-reviewed · Nature Biotechnology · 2019

peer-reviewed · Nature Biotechnology · 2019. Arun Devabhaktuni et al. Although mass spectrometry is well suited to identifying thousands of potential protein post-translational…
Date 2019-04-01
Type peer-reviewed
Venue Nature Biotechnology
Publisher Springer Science and Business Media LLC
Contribution adjacent
DOI 10.1038/s41587-019-0067-5
Citations (OpenAlex) 164
Venue 2-year citedness 12.89

Abstract

Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.

Authors

  1. Arun Devabhaktuni · Stanford Medicine, Stanford University
  2. Sarah Lin · Stanford Medicine, Stanford University
  3. Lichao Zhang · Stanford Medicine, Stanford University, University of Virginia
  4. Kavya Swaminathan · Stanford Medicine, Stanford University
  5. Carlos G. Gonzalez · Stanford Medicine, Stanford University
  6. Niclas Olsson · Stanford Medicine, Stanford University
  7. Samuel M. Pearlman · Stanford Medicine, Stanford University
  8. Keith Rawson · Stanford Medicine, Stanford University
  9. Joshua E. Elias · Stanford Medicine, Stanford University

Methods and tools

  • TagGraph: Matches de novo sequence tags against a proteome with an unrestricted string-based search and a probabilistic model, identifying thousands of modification types across large datasets.

Methods it uses

  • PEAKS: Commercial DP-based de novo

Data deposited

  • TagGraph re-analysis of a lung proteome (Peptide Atlas PAe001771), which was a component of the Wilhelm et al human proteome map — as deposited · PXD008902

Cites (5)

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