Although proteomics has made rapid progress in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a "bird view" of intact proteoforms. The combinatorial explosion of possible proteoforms, which may result in billions of possible proteoforms for one protein, makes proteoform identification a challenging computational problem. Here we propose a new data structure, called the mass graph, for efficiently representing proteoforms. In addition, we design mass graph alignment algorithms for proteoform identification by top-down mass spectrometry. Experiments on a histone H4 mass spectrometry data set showed that the proposed methods outperformed MS-Align-E in identifying complex proteoforms.
Mass graphs and their applications in top-down proteomics