Beyond Lab-Specific Calibration

Georg Wallman, co-founder and managing director of Aplusia, sat down with LCGC International at the ASMS conference to discuss the cutting edge of AI-driven mass spectrometry. Wallman's company operates as a biotech consultancy, specializing in challenging drug discovery problems for pharma and biotech clients, and his presentation at ASMS gave a vivid picture of just how rapidly the field is moving. The centerpiece of Wallman's talk was PeptDeepKontext, a universal deep learning model designed to predict peptide properties—including spectra, retention times, and charge states—across diverse instruments, laboratory conditions, and post-translational modifications.¹ The model addresses a long-standing problem: existing tools tend to fail when taken out of the specific setup in which they were trained. PeptDeepKontext instead treats inter-lab variability not as a nuisance to eliminate, but as a dimension to learn from, drawing on data from more than 50 studies spanning a wide range of LC instruments, mass spectrometers, solvents, and gradient profiles.

In this interview clip, Wallman discusses:

• Instrument-to-instrument variation can be a pain point, but labs differ a lot in how they're set up. Does PeptDeepKontext need any lab-specific calibration data to perform well?  

Wallman also introduced fragDETR, a graph-level fragmentation approach borrowed from metabolomics and adapted for peptides. The technology unlocks richer spectral predictions —accounting for internal fragments, neutral losses, and side-chain fragmentation—yielding up to three times more identified peaks and five times greater intensity gains in challenging cases such as heavily modified peptides and non-tryptic sequences. Looking beyond instrumentation, Wallman offered a compelling perspective on where proteomics sits within pharmaceutical drug discovery. Once a niche core-facility tool, proteomics is now capable of processing hundreds of samples per day and is increasingly positioned at the centre of high-throughput drug discovery platforms, particularly for covalent drug programs and targeted protein degradation. As instrument capabilities continue to outpace what biology alone demands, Wallman sees a transformative period ahead for the field.

Reference

1. Wallman, G.; Kotb, M.; Lebedev, M.; Müller-Reif, J. B.; Mann, M. PeptDeepKontext: A Universal Model for Predicting Peptide Properties Across Instruments, Conditions, and Post-Translational Modifications Trained on a New Foundational Dataset. Presented at ASMS 2026, in San Diego, California, USA. https://asms.org/docs/default-source/conference/74th-asms-final-program_as-of-may-8-2026.pdf?sfvrsn=1234fc3_0 (accessed 2026-06-24).