MSNovo: A Dynamic Programming Algorithm for de Novo Peptide Sequencing via Tandem Mass Spectrometry

peer-reviewed · Analytical Chemistry · 2007

peer-reviewed · Analytical Chemistry · 2007. Lijuan Mo et al. Tandem mass spectrometry (MS/MS) has become the experimental method of choice for high-throughput…

Date	2007-06-15
Type	peer-reviewed
Venue	Analytical Chemistry
Publisher	American Chemical Society (ACS)
Contribution	algorithm
DOI	10.1021/ac070039n
Citations (OpenAlex)	95
Venue 2-year citedness	7.10

Abstract

Tandem mass spectrometry (MS/MS) has become the experimental method of choice for high-throughput proteomics-based biological discovery. The two primary ways of analyzing MS/MS data are database search and de novo sequencing. In this paper, we present a new approach to peptide de novo sequencing, called MSNovo, which has the following advanced features. (1) It works on data generated from both LCQ and LTQ mass spectrometers and interprets singly, doubly, and triply charged ions. (2) It integrates a new probabilistic scoring function with a mass array-based dynamic programming algorithm. The simplicity of the scoring function, with only 6-10 parameters to be trained, avoids the problem of overfitting and allows MSNovo to be adopted for other machines and data sets easily. The mass array data structure explicitly encodes all possible peptides and allows the dynamic programming algorithm to find the best peptide. (3) Compared to existing programs, MSNovo predicts peptides as well as sequence tags with a higher accuracy, which is important for those applications that search protein databases using the de novo sequencing results. More specifically, we show that MSNovo outperforms other programs on various ESI ion trap data. We also show that for high-resolution data the performance of MSNovo improves significantly. Supporting Information, executable files and data sets can be found at http://msms.usc.edu/supplementary/msnovo.

Authors

Methods and tools

MSNovo: Mass-array dynamic programming

Cites (9)

De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry (2007) crossref
NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing (2005) crossref
PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) crossref
Shotgun Protein Sequencing by Tandem Mass Spectra Assembly (2004) crossref
Sequit: software for de novo peptide sequencing by matrix-assisted laser desorption/ionization post-source decay mass spectrometry (2004) crossref
A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry (2001) crossref
De novo peptide sequencing via tandem mass spectrometry (1999) crossref
Sequence database searches via de novo peptide sequencing by tandem mass spectrometry (1997) crossref
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Classification of B and Y Ions in Peptide MS/MS Spectra Based on Machine Learning (2023) both
The Current State-of-the-Art Identification of Unknown Proteins Using Mass Spectrometry Exemplified on De Novo Sequencing of a Venom Protease from Bothrops moojeni (2022) both
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