The hardest part of studying microproteins is simply seeing them. Standard gene annotation was not built to flag very short coding sequences, and standard proteomics was not built to detect very small proteins. ORFeus closes that gap by developing and combining methods that each capture part of the picture: ribosome profiling to read what a cell is actively translating, mass-spectrometry proteomics and proteogenomics to confirm the proteins are really there, and immunopeptidomics to capture the fragments displayed to the immune system. Advanced machine learning ties the evidence together and helps decide which candidates are worth pursuing.
First, build the map. WP1 develops the experimental and computational tools to detect non-canonical translation reliably and at scale, and produces the reference datasets the rest of the network depends on: new ribosome-profiling methods, cross-species prediction, and large catalogs of condition-specific translation.
Next, ask what they do. WP2 moves from detection to function: mapping how microproteins interact and fold, designing molecules to switch their activity on or off, reading their evolutionary signatures for clues to importance, and resolving translation down to single cells.
Finally, put it to work. WP3 focuses on disease: how cancer-specific and stress-induced microproteins are displayed to the immune system, which ones make credible neoantigens, and how that knowledge could feed immunotherapies and vaccines.
The network’s science is carried by 15 connected PhD projects. Each is hosted by a different group, co-supervised across institutions, and linked to a partner. They are designed to feed one another: data and tools from one project become the inputs for several others.
University of Southampton (UK)
Can a machine learn to recognize a real microprotein gene from its DNA alone? This project builds AI models that predict which short ORFs are genuinely translated, trained on data from across the network and released as open tools for the wider community.
Supervisor: Owen Rackham
Co-supervisor: Uwe Ohler
Max Delbrück Center, Berlin (DE)
What can single molecules tell us about translation that bulk methods cannot? This project develops new ways to read which ORFs are translated from individual RNA molecules, resolving how different transcript forms carry different proteins.
Supervisor: Uwe Ohler
Co-supervisor: Eivind Valen
University of Oslo (NO)
Most translation events are common enough to detect in a single experiment, but what about the rare ones? This project mines more than 15,000 public ribosome profiling datasets to find translation events that only appear under specific conditions or in specific species.
Supervisor: Eivind Valen
Co-supervisor: Pasha Baranov
Institute of Cancer Research, London (UK)
When a bacterium infects a cell, both host and pathogen produce microproteins. This project maps that landscape using dual proteomics and ribosome profiling, asking which microproteins shape the course of infection.
Supervisor: Jyoti Choudhary
Co-supervisor: Petra Van Damme
University of Freiburg (DE)
Many microproteins are secreted from cells, but current methods largely miss them. This project develops chemical tagging approaches to capture secreted microproteins and identify the cell-surface receptors they bind to.
Supervisor: Simon Elsässer
Co-supervisor: Jyoti Choudhary
Hubrecht Institute (KNAW), Utrecht (NL)
Once you find a microprotein, how do you work out what it does? This project uses computational protein design to create synthetic molecules that bind specific microproteins and switch their activity on or off.
Supervisor: Danny Sahtoe
Co-supervisor: Owen Rackham
University of Leeds (UK)
Long non-coding RNAs were long assumed not to make proteins, but some of them do. This project investigates the rules that govern when and how ribosomes translate these unexpected transcripts, focusing on neuronal development.
Supervisor: Julie Aspden
Co-supervisor: M. Mar Albà
Hospital del Mar Research Institute (HMRIB-CERCA), Barcelona (ES)
Evolution leaves traces. If a microprotein has been conserved across species, it is more likely to matter. This project uses evolutionary analysis and functional screens to predict which newly discovered microproteins are biologically important.
Supervisor: M. Mar Albà
Co-supervisor: Simon Elsässer
Hubrecht Institute (KNAW), Utrecht (NL)
Different cells in the same tissue can translate the same gene differently. This project develops a new method that combines CRISPR editing with single-cell ribosome profiling to study translation regulation one cell at a time.
Supervisor: Alexander van Oudenaarden
Co-supervisor: Julie Aspden
Ghent University (BE)
How does removing a single microprotein change the behavior of a whole proteome? This project uses thermal proteome profiling in bacteria to measure how protein stability and interactions shift when a microprotein is absent, building a functional map of the microproteome.
Supervisor: Petra Van Damme
Co-supervisor: Danny Sahtoe
University of Dundee (UK)
HPV rewrites the host cell’s RNA landscape, and some of the resulting hybrid transcripts may encode new proteins. This project maps non-canonical translation in HPV-driven cancers and tests whether the resulting peptides can be recognized by the immune system.
Supervisor: Nicola Ternette
Co-supervisor: Sebastiaan van Heesch
University College Cork (IE)
Common genetic variants can change which proteins a cell makes, even outside the known gene catalog. This project studies how variation in non-coding regions alters translation, and which of these changes are relevant in cancer.
Supervisor: Pasha Baranov
Co-supervisor: Alexander van Oudenaarden
Princess Máxima Center, Utrecht (NL)
What happens when cancer cells start reading their genome in unexpected ways? This project studies how tumour cells switch on a hidden layer of protein coding under stress, and what the resulting dark proteome means for cancer biology and immunotherapy. The work runs alongside ILLUMINE, the Cancer Grand Challenges project on the dark proteome.
Supervisor: Sebastiaan van Heesch
Co-supervisor: Reuven Agami
Netherlands Cancer Institute (NKI-AVL), Amsterdam (NL)
For a microprotein to become an immune target, the cell must process and display it on its surface. This project uses CRISPR screens to find the factors that control this display, identifying regulators that could be targeted to make tumours more visible to the immune system.
Supervisor: Reuven Agami
Co-supervisor: Michal Bassani-Sternberg
University of Lausanne (CH)
Which hidden peptides actually reach the cell surface and stay there long enough for the immune system to find them? This project combines immunopeptidomics with machine learning to predict which cryptic peptides make credible targets for cancer immunotherapy.
Supervisor: Michal Bassani-Sternberg
Co-supervisor: Uwe Ohler