Antilope - A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem

TL;DR

Antilope introduces a fast, flexible Lagrangian relaxation-based method for de novo peptide sequencing from mass spectrometry data, outperforming existing tools in speed and competitiveness in accuracy.

Contribution

The paper presents Antilope, a novel approach combining Lagrangian relaxation with shortest path algorithms for peptide sequencing, offering improved speed and flexibility over prior methods.

Findings

Antilope significantly reduces running time compared to mixed integer optimization.

It performs comparably to PepNovo and NovoHMM in accuracy.

Offers increased flexibility in ion type consideration.

Abstract

Peptide sequencing from mass spectrometry data is a key step in proteome research. Especially de novo sequencing, the identification of a peptide from its spectrum alone, is still a challenge even for state-of-the-art algorithmic approaches. In this paper we present Antilope, a new fast and flexible approach based on mathematical programming. It builds on the spectrum graph model and works with a variety of scoring schemes. Antilope combines Lagrangian relaxation for solving an integer linear programming formulation with an adaptation of Yen's k shortest paths algorithm. It shows a significant improvement in running time compared to mixed integer optimization and performs at the same speed like other state-of-the-art tools. We also implemented a generic probabilistic scoring scheme that can be trained automatically for a dataset of annotated spectra and is independent of the mass spectrometer type. Evaluations on benchmark data show that Antilope is competitive to the popular state-of-the-art programs PepNovo and NovoHMM both in terms of run time and accuracy. Furthermore, it offers increased flexibility in the number of considered ion types. Antilope will be freely available as part of the open source proteomics library OpenMS.