A graph-based approach for modification site assignment in proteomics

TL;DR

This paper introduces a graph-based algorithm for assigning post-translational modifications to peptide sites in proteomics, improving accuracy and efficiency in complex modification scenarios.

Contribution

It presents a novel graph-based method for optimal modification site assignment that handles conflicting scores and scales efficiently.

Findings

Accurately assigns modifications in complex peptides

Operates efficiently on thousands of peptides

Improves spectrum annotation and biological interpretation

Abstract

Background In proteomics, the most probable localizations of post-translational modifications are assessed by localization scores evaluating the likelihood of a given modification to occupy a site on a peptide sequence. When identifying highly modified peptides, localization scores for different modifications can return conflicting results, stacking modifications on the same amino acid. Here, we propose a graph-based approach that assigns modifications to sites in a way that maximizes localization scores while avoiding conflicting assignments. Results The algorithm is implemented as both a standalone Python program and in the compomics-utilities Java library. Our graph-based approach showed the ability to match complex combinations of modifications and acceptor sites, allowing the processing of thousands of peptides in a few seconds. Conclusions Our graph-based approach to modification site assignment allows distributing multiple modifications in a way that maximizes individual localization scores. Having an optimal modification site assignment is important for spectrum annotation and biological interpretation.