Casper A. Goverde

This study computationally designs soluble analogues of animal and microbial rhodopsins, showing they are well-folded, thermostable, and retain retinal photocycle functionality.

The paper reports cryo-EM structures of foamy virus Env in pre- and post-fusion states, revealing structural similarity to paramyxovirus F proteins and suggesting an evolutionary link.

This paper introduces a deep learning framework called Genesis to learn protein structural patterns and demonstrates its ability to design novel protein folds with high protease resistance.

Hallucinating binders by backpropagating through AlphaFold2 weights gave, across 12 targets, binders in 10-100% of the designs tested per target, mostly at nanomolar affinity.

The paper predicts homo-oligomerization across four proteomes using AlphaFold2, finding that 50% of archaea, 45% of bacteria, and 20% of eukaryotes form homomers, with disease mutations enriched at interfaces.

AF2BIND predicts ligand-binding sites using AlphaFold2's pairwise representation without requiring a ligand, outperforming other models and linking bait amino acids to ligand properties.

This paper inverts the AlphaFold2 network to perform de novo protein design, showing that while the method generates viable sequences with correct folds, it requires post-design optimization to achieve optimal surface properties.

This study designs soluble analogues of integral membrane proteins using deep learning, achieving high thermal stability and functional accuracy.