July 4 – 9 , 2021, Dagstuhl Seminar 21271

Computational Proteomics


Sebastian Böcker (Universität Jena, DE)
Rebekah Gundry (University of Nebraska – Omaha, US)
Lennart Martens (Ghent University, BE)
Magnus Palmblad (Leiden University Medical Center, NL)

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Dagstuhl Report, Volume 11, Issue 6 Dagstuhl Report
Aims & Scope
List of Participants
Dagstuhl's Impact: Documents available
Dagstuhl Seminar Schedule [pdf]


The Dagstuhl Seminar 21271 "Computational Proteomics" discussed several important developments, challenges, and opportunities that are emerging in the field of computational proteomics. Three core topics were set out at the start, and these were discussed at length throughout the seminar.

These three topics were: (i) the fast evolving use of advanced machine learning approaches in proteomics; (ii) the challenges and opportunities offered by fast developing approaches for structural and top-down proteomics; and (iii) specific issues and computational complications in glycoproteomics.

The machine learning and glycoproteomics topics were each introduced by a dedicated lecture, which set out the current state-of-the-art and presented a tentative set of issues, challenges, or opportunities that could be explored during the seminar. The structural and top-down proteomics topic was introduced by two sequential lectures, one on structural proteomics, and one on top-down proteomics. In total, four introductory talks were thus presented at the start of the seminar. For each of the three main topics, daily Working Group sessions were organised, which took place in the morning and afternoon, with a daily late-night session scheduled each day to wrap up the day's outcomes. This structure was followed to allow maximum involvement by online participants across the various timezones in the hybrid format. The Machine Learning in Proteomics Working Group also spun out another Working Group session during the seminar, which discussed the creation of a machine learning (Kaggle-like) competition based on proteomics data.

Each of these breakout sessions was very actively attended, including by online attendees, and resulted in several interesting research ideas and potential new initiatives. The Machine Learning in Proteomics Working Group was the largest working group, and addressed a number of distinct topics during the seminar. Of particular note were the spin-out effort to establish two machine learning competitions based on proteomics data and challenges to engage the broader machine learning community, and the extensive discussions on the optimal way to represent mass spectrometry data for downstream machine learning.

The Glycoproteomics Working Group was very actively attended, and discussed an exciting set of topics. A first highlight among these topics was provided by the extensive and detailed discussions with the Machine Learning Working Group regarding the potential of, and road towards, the use of state-of-the-art machine learning approaches in glycoproteomics. A second highlight concerned the delineation of a set of high-impact opinion papers to describe the state-of-the-art of the field, and its goals, ambitions, and challenges.

The Structural and Top-Down Proteomics Working Group was very active in detailing the many challenges and opportunities in this fast-evolving field. One noteworthy challenge revolved around the detection, annotation, and biological interpretation of post-translational modifications detected by mass spectrometry. A second challenge concerned the standardization of acquired native mass spectrometry data, the minimal reporting requirements for these experiments, and the dissemination of these data.

Overall, the 2021 Dagstuhl Seminar on Computational Proteomics was extremely successful as a catalyst for careful yet original thinking about key challenges in the field, and as a means to enable downstream progress by setting important, high impact goals to work on in close collaboration. During this Seminar, new topics for a future Seminar were suggested throughout as well, indicating that this active field will continue to yield novel challenges and opportunities for advanced computational work going forward.

Summary text license
  Creative Commons BY 4.0
  Lennart Martens, Rebekah Gundry, and Magnus Palmblad

Dagstuhl Seminar Series


  • Machine Learning
  • Other Computer Science


  • Bioinformatics
  • Proteomics
  • Computational Mass Spectrometry
  • Machine Learning


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