A post-translational modification (PTM) is an alteration on a protein that changes the amino acid sequence into a functional proteoform. It can regulate structure, stability, cellular localization and function of protein and, therefore, affect almost all cellular processes. However, with over 200 different known PTMs, the overall complexity of the proteome is exponentially increased impeding confident identification and quantification using biochemistry and mass spectrometry. In recent years, the heterogeneity across samples and interactions between PTMs, called cross-talk, have garnered broader interest through associations with diseases such as cancer, diabetes, and Alzheimer"s disease. Identifying dependencies between PTMs on the same protein has been a challenge both in the shotgun and top-down approaches of proteomic mass spectrometry. While approaches for identification of various PTMs exist, PTM-centric proteoform assembly methods are lacking to further investigate PTM cross-talk.

To address this issue, we have developed an approach which is able to reconstruct the proteoforms from shotgun proteomics experiments. Using an expectation maximization algorithm, we iteratively exclude unlikely combinations of peptides with and without PTMs to maximize the probability for the most likely set of proteoforms supported by the underlying peptide and PTM data. Thereby, we provide the most likely set of only few proteoforms that can be synthesized in a laboratory and used for targeted analyses. We tested our approach by simulating shotgun mass spectrometry experiments for a given set of ground truth proteoforms. Our results show high accuracy and precision. And additional benefit of our approach is the post-hoc processing capability to vast amounts of publicly available proteomics data, making cross species, cross tissue, and cross condition comparisons of PTM-centric proteoforms possible.

With the knowledge gained from reconstructing the most likely set of proteoforms and therefore the identified co-expression of PTMs, we hope to provide researchers with a tool to perform targeted analyses with biochemistry and synthesizes versions of the proteoforms. This will provide new insights into cellular protein regulation and, furthermore, highlight sets of PTMs that are of particular interest in a variety of human tissues and conditions. Additionally, personal PTM patterns could be identified that can help develop new and targeted treatments for various diseases.