Bruker Corporation (Nasdaq: BRKR) announced several new workflows for its timsUltra AIP and timsOmni mass spectrometry platforms at the Human Proteome Organization World Congress 2026 in Singapore, targeting drug discovery and disease biology research.

A multi-enzyme dia-PASEF method for the timsUltra AIP instrument combined complementary protease digests to yield approximately 350,000 peptides per sample in Parkinson's brain tissue, identifying around 14,500 protein isoforms corresponding to more than 10,000 canonical proteins, according to the company.

Bruker's ProteoScape software now provides a direct path from discovery proteomics to targeted validation using prm-PASEF, allowing researchers to convert discovery results into targeted protein and biomarker assays on the same timsTOF platform.

Spectronaut v21, developed by Biognosys, introduces AI transfer learning to adapt analytical models to new mass spectrometry acquisition modes without requiring full model redevelopment from large training datasets.

The company also introduced Guided PASEF EXciD, a timsOmni workflow operating at more than 30 Hz for glycopeptide characterization. The method identifies glycan ions during acquisition and directs fragmentation to selected precursors while retaining ion mobility separation.

Bruker and Affipro Analytics announced a co-marketing and development agreement to integrate hydrogen-deuterium exchange mass spectrometry workflows, combining Affipro's automation and analysis software with Bruker's HyStar platform control for protein conformation and interaction studies.

For nucleic acid analysis, the timsOmni platform demonstrated complete sequence coverage of RNA and DNA oligonucleotides using electron detachment dissociation and Omnitrap MS³ workflows, including analysis of the antisense drug Fomivirsen. Native top-down analysis of folded RNA structures was also demonstrated.

Frank H. Laukien, President and CEO of Bruker, said the workflows aim to combine "proteome depth, PTMs, proteoforms, protein interactions, and conformational and spatial context to enable richer multimodal training data for next-generation biological AI models."