To measure, model, and predict protein folding dynamics
in health and disease.
The Scientific Problem
Why static snapshots aren't enough
Most structural methods provide only static snapshots — missing the dynamic reality of folding.
Protein folding is a dynamic, non-linear process involving transient intermediates, molecular chaperones, and complex cellular environments.
We lack predictive models linking folding pathways to specific pathologies. To predict and prevent misfolding, we must observe it in real time and in living systems.
Core Objectives
Core Objectives
Five aims of our research agenda
01
Protein dynamics
Observe protein folding and misfolding as dynamic processes in living systems
Capture transient intermediates that determine function and failure
Resolve folding pathways with single-molecule precision
02
Integrated experimental technologies
Combine force spectroscopy, advanced microscopy, and photonics
Link complementary methods into a unified experimental platform
Enable multiscale measurements from molecules to cells
03
Predictive AI and simulations
Develop AI models that learn directly from experimental data
Predict folding pathways, misfolding events, and failure points
Integrate simulations with real-time measurements for validation
04
Link folding to disease
Connect molecular folding dynamics to disease-related processes
Identify biomarkers linked to protein misfolding
Translate fundamental insights into clinical and biomedical applications
05
National coordination and global leadership
Align Swedish expertise, infrastructure, and training efforts
Build a shared national roadmap for protein folding research
Position Sweden as an international leader in dynamic protein science