A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry

TL;DR

This paper presents a dynamic programming algorithm for de novo peptide sequencing from tandem mass spectrometry data, efficiently reconstructing peptide sequences and identifying modifications despite noise.

Contribution

The authors introduce a novel dynamic programming method that efficiently solves peptide sequencing and modification detection problems using spectral graph representations.

Findings

Algorithm runs in linear time relative to graph size

Successfully applied to real experimental data

Can detect amino acid modifications and handle noise

Abstract

The tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged prefix and suffix subsequences, and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G=(V,E) where |V|=2k+2, we can solve this problem in O(|V|+|E|) time and O(|V|) space using dynamic programming. Our approach can be further used to discover a modified amino acid in O(|V||E|) time and to analyze data with other types of noise in O(|V||E|) time. Our algorithms have been implemented and tested on actual experimental data.