Protein structure and complex prediction
From consensus and quality assessment methods to AlphaFold-era modelling of protein interactions and higher-order assemblies.
Stockholm University · SciLifeLab
Computational protein science at scale
The Elofsson Lab develops computational methods to understand protein structure, protein interactions, membrane proteins, and protein evolution, increasingly powered by deep learning and large-scale structural data.
Current focus
- Protein interactions and complex assembly
- Domain evolution at AlphaFoldDB scale
- Membrane protein topology and structure
- Machine learning for proteoforms and design
Research areas
The group combines structural bioinformatics, machine learning, and evolutionary analysis.
Protein evolution and domain architecture
How domains change, duplicate, and rearrange across the tree of life, now with structure-aware analysis using AlphaFoldDB, TED, and ECOD.
Membrane proteins and topology
Methods and mechanistic insight for membrane protein topology, transporters, and membrane-protein structure.
AI for proteoforms and molecular interactions
Representation learning, graph methods, and protein language models for proteoforms, interactions, and design.
181+
Publications listed in the current CV
30 MSEK
KAW project: Learning the language of the cell
4.2 MSEK
VR 2025 project on protein domain architecture evolution
5
Current PhD students listed in the CV
About the lab
The lab led by Arne Elofsson develops computational methods for protein science, with longstanding contributions in protein structure prediction, membrane protein topology prediction, protein model quality assessment, and protein evolution. The current portfolio extends these strengths into AlphaFold-era structural biology, large-scale protein interaction modelling, and AI-driven analysis.