New computational approaches for de novo peptide sequencing from MS/MS experiments

peer-reviewed · Proceedings of the IEEE · 2002

peer-reviewed · Proceedings of the IEEE · 2002. O. Lubeck et al. We describe computational methods to solve the problem of identifying novel proteins from tandem mass…
Date 2002-12-01
Type peer-reviewed
Venue Proceedings of the IEEE
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Contribution algorithm
DOI 10.1109/JPROC.2002.805301
Citations (OpenAlex) 22
Venue 2-year citedness 8.64

Abstract

We describe computational methods to solve the problem of identifying novel proteins from tandem mass spectrometry (tandem MS or MS/MS) data and introduce new approaches that will give more accurate solutions. These new approaches integrate chemical information and knowledge into a graph-theoretic framework. Two sources of chemical information that we investigate are mass tagging and dissociation chemistry in the tandem MS process itself. We describe machine learning techniques that are used to classify peaks according to ion types based on known dissociation chemistry. We describe the algorithms that are implemented in a software code called PepSUMS. Using PepSUMS, we give results on the effectiveness of the new methods on the ultimate goal of improved protein identification.

Authors

  1. O. Lubeck · Los Alamos National Laboratory
  2. Carrock Sewell · Los Alamos National Laboratory
  3. Sheng Gu · Los Alamos National Laboratory
  4. Xian Chen · Los Alamos National Laboratory
  5. D. M. Cai · Los Alamos National Laboratory

Methods and tools

  • PepSUMS: Computational framework integrating mass tags and dissociation chemistry for de novo peptide sequencing from MS/MS experiments.

Cites (2)

Cited by (8)

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