Moritz Heusel

The paper introduces mE-DIA, a multi-enzyme DIA-MS workflow that improves proteoform detection in proteomics by integrating analysis and interpretation into a single toolset.

DIP-MS resolves protein complex isoforms using deep learning and mass spectrometry, revealing new subunits in human prefoldin complexes.

This study develops multienzyme deep learning models for de novo peptide sequencing from mass spectrometry data, showing improved generalizability and sequence coverage across diverse protease digests and species.

The paper presents a rapid workflow combining experimental and computational methods to profile protein complex re-organization in cellular states, showing distinct changes during differentiation and macrophage stimulation.

This paper introduces a data-driven method to detect functional proteoform groups from bottom-up proteomic datasets, identifying state-specific proteoform groups linked to molecular mechanisms.

SECAT quantifies differential protein-protein interaction states across cellular states by analyzing PPI networks, showing improved scalability and effectiveness over previous methods.

The paper introduces PCprophet, a computational framework for identifying and analyzing protein complexes from co-fractionation mass spectrometry data across conditions.

The paper quantifies proteome assembly state changes during mitosis using SEC-SWATH-MS and identifies new mitotic complex disassembly events.

The paper introduces a method to quantify protein complexes using SEC-SWATH-MS and identifies 462 complexes from HEK293 cells.