UVnovo: A de Novo Sequencing Algorithm Using Single Series of Fragment Ions via Chromophore Tagging and 351 nm Ultraviolet Photodissociation Mass Spectrometry

peer-reviewed · Analytical Chemistry · 2016

peer-reviewed · Analytical Chemistry · 2016. Scott A. Robotham et al. De novo peptide sequencing by mass spectrometry represents an important strategy for characterizing novel…
Date 2016-04-26
Type peer-reviewed
Venue Analytical Chemistry
Publisher ACS
Contribution algorithm
DOI 10.1021/acs.analchem.6b00261
Citations (OpenAlex) 26
Venue 2-year citedness 6.29

Abstract

De novo peptide sequencing by mass spectrometry represents an important strategy for characterizing novel peptides and proteins, in which a peptide’s amino acid sequence is inferred directly from the precursor peptide mass and tandem mass spectrum (MS/MS or MS(3)) fragment ions, without comparison to a reference proteome. This method is ideal for organisms or samples lacking a complete or well-annotated reference sequence set. One of the major barriers to de novo spectral interpretation arises from confusion of N- and C-terminal ion series due to the symmetry between b and y ion pairs created by collisional activation methods (or c, z ions for electron-based activation methods). This is known as the “antisymmetric path problem” and leads to inverted amino acid subsequences within a de novo reconstruction. Here, we combine several key strategies for de novo peptide sequencing into a single high-throughput pipeline: high-efficiency carbamylation blocks lysine side chains, and subsequent tryptic digestion and N-terminal peptide derivatization with the ultraviolet chromophore AMCA yield peptides susceptible to 351 nm ultraviolet photodissociation (UVPD). UVPD-MS/MS of the AMCA-modified peptides then predominantly produces y ions in the MS/MS spectra, specifically addressing the antisymmetric path problem. Finally, the program UVnovo applies a random forest algorithm to automatically learn from and then interpret UVPD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring. We show this combined strategy provides high-performance de novo peptide sequencing, enabling the de novo sequencing of thousands of peptides from an Escherichia coli lysate at high confidence.

Authors

  1. Scott A. Robotham · University of Texas at Austin
  2. Andrew P. Horton · University of Texas at Austin
  3. Joe R. Cannon · University of Texas at Austin
  4. Victoria C. Cotham · University of Texas at Austin
  5. Edward M. Marcotte · University of Texas at Austin
  6. Jennifer S. Brodbelt · University of Texas at Austin

Methods and tools

  • UVnovo: UV photodissociation de novo

Cites (15)

Cited by (7)

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