Institute for Protein Design

Diffusion-based backbone generation and sequence design method for programmable asymmetric transmembrane beta-barrel nanopores.

All-atom protein design diffusion model conditioned on ligands, nucleic acids, and other non-protein atoms, supporting enzyme and DNA binder design.

Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.

Nucleic acid inverse-folding network that designs RNA sequences for a target 3D backbone and predicts protein-DNA binding specificity.

All-atom structure prediction for arbitrary biomolecular complexes of proteins, nucleic acids, and ligands, with code and weights under a BSD license.

Protein sequence design model that represents small molecules, nucleotides, and metals at atomic resolution, enabling ligand-aware enzyme design.

Macrocyclic peptide binder design against protein targets, cyclizing a diffusion backbone generator's positional encoding so it closes rings.

De novo enzyme design conditioned on the reaction to be catalysed: substrate and product SMILES in, catalytic pocket, enzyme, and docked complex out.

Enzyme catalytic pocket design conditioned on a reaction: substrate and product in, pocket backbone, sequence, and EC class out.

Structure-based mutational effect prediction from local atomic environments, scoring how substitutions change protein stability and binding affinity.

De novo protein design diffusion model that generates backbone structures conditioned on binding targets, symmetry constraints, and functional motifs.

Protein structure prediction for monomers and complexes in one three-track network, scaling past 1000 residues without triangle attention.

AlphaFold fine-tuned on peptide-MHC and protein-peptide binding data for specificity prediction across MHC class I/II, PDZ, and SH3 domains.

Cyclic peptide structure prediction and de novo macrocycle design, by wrapping a frozen structure predictor's positional encoding into a ring.

Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.

Protein-nucleic acid complex structure prediction from sequence, folding protein, DNA and RNA chains in one network with confidence estimates.

Full-atom protein model accuracy estimation, regressing per-atom lDDT with an SE(3)-transformer over a heavy-atom graph of the modeled structure.

Structure accuracy estimation over atom graphs, scoring macrocyclic peptide and protein-DNA models that residue-level predictors cannot represent.

Protein structure and complex prediction from sequence, in a three-track network that reasons over alignments, distances, and 3D coordinates at once.

Protein model accuracy estimation predicting per-residue lDDT plus signed residue-pair distance errors that become Rosetta refinement restraints.