BoltzGen glycan-aware binder design
BoltzGen is Boltz-2 binder design with native support for glycans, post-translational modifications, and non-canonical amino acid residues. The features other de novo binder design tools quietly strip out before generation. Every candidate is refolded end-to-end on the same Boltz-2 model used for design.
Hosted self-serve at tools.ranomics.com. No CCD-stripping, no PTM reversion, no manual preprocessing.
For targets where chemistry matters: antibody Fc with N-glycans, viral spike glycoproteins, phosphorylated kinase regulators, mucins.
From glycosylated target to refolded binders in one pass
Define the target
Upload a PDB or mmCIF with glycans, PTMs, or non-canonical residues intact. CCD codes (NAG, MAN, SEP, TPO, MLY, etc.) are passed straight to Boltz-2. No manual stripping required.
BoltzGen generation
Boltz-2 samples binder candidates conditioned on the full chemical environment of the target. Modified residues and attached glycans are visible to the model during design, not erased.
End-to-end refold
Each generated complex is refolded by the same Boltz-2 model and scored on ipTM, pLDDT, and PAE. No swap to a separate validator that may not understand the modifications.
Ranked candidates
Top designs returned with confidence metrics, designed sequence, and a PDB of the predicted complex. Ready for ProteinMPNN refinement, yeast display validation, or downstream wet-lab handoff.
Boltz-2 as both generator and validator
BoltzGen is built on Boltz-2 (Passaro et al., 2025), the open-source biomolecular foundation model that follows Boltz-1 (Wohlwend et al., bioRxiv 2024, "Democratizing Biomolecular Interaction Modeling"). Five components define what BoltzGen does that other generators do not.
Boltz-2 architecture
A pair-representation transformer trained on PDB complex structures with affinity heads added. AlphaFold3-class accuracy, fully open weights, and joint modeling of structure and binding. The same model writes the design and grades the fold.
Glycans as first-class entities
N-linked and O-linked glycans (NAG, MAN, BMA, FUC, SIA, GAL and the full CCD library) attached to the target are visible to Boltz-2 during generation. The model designs around them rather than pretending they are not there.
PTM-aware design
Phosphorylation (SEP/TPO/PTR), acetylation, methylation, hydroxylation, and any other CCD-coded post-translational modification on the target chain is passed through to the model. Binders can be designed against the modified state directly.
Non-canonical residue support
Selenomethionine, pyrrolysine, hydroxyproline, and other modified amino acids in the target are handled natively. Useful for engineered or synthetic protein targets that other binder design tools cannot ingest without manual mutation back to canonical residues.
End-to-end refold scoring
Every generated complex is refolded by Boltz-2 itself and scored on ipTM (interface predicted TM-score), pLDDT (per-residue confidence), and PAE (predicted aligned error). No hand-off to a separate validator that may have been trained on different chemistry.
Glycan-aware design is not optional for half of pharma targets
When chemistry shapes the surface
Antibody Fc engagement, viral spike glycoprotein neutralization, mucin-domain targeting, and phospho-regulated kinase scaffolds all have non-amino-acid features that dominate the binding interface. Strip them and you are designing against a protein that does not exist in cells.
What other generators ignore
RFdiffusion and BindCraft both require glycans stripped and PTMs reverted to the parent residue before generation. The resulting binders may clash with the real glycoform once expressed. BoltzGen sees the target chemistry the cell sees.
One model, design and validate
Most binder design pipelines pair a generator (RFdiffusion) with a separate validator (AF2 multimer). BoltzGen uses Boltz-2 for both, so the confidence score on a candidate reflects the same model and chemistry that produced it. No train-test mismatch on modifications.
The right tool when the target is more than a peptide chain
For a clean cytoplasmic target with no modifications, RFdiffusion or BindCraft are the workhorses. BoltzGen earns its place when the target carries glycans, post-translational modifications, or non-canonical residues that matter for the binding interface.
Run BoltzGen first when the modifications are at or near the candidate epitope. Combine BoltzGen with RFdiffusion outputs when you want maximum diversity and the modifications are peripheral.
Antibody Fc engineering where N297 glycosylation drives FcγR engagement
Viral spike glycoproteins (HIV Env, SARS-CoV-2 S, influenza HA) with dense N-glycan shielding
Phosphorylated kinase regulatory loops where pSer/pThr is the recognition motif
Mucin-domain targets where O-linked glycans define the surface
Engineered protein targets containing selenomethionine, hydroxyproline, or other non-canonical residues
Acetylated histone tails and other PTM-marked epigenetic readers
Any target where the published co-crystal structure includes ligands or modifications you need to design around
From in-silico binders to validated hits in cells
BoltzGen returns a ranked list of designed binder sequences with predicted complex structures. If your target has glycans, PTMs, or non-canonical residues, this is the design tool to use. The next step is wet-lab validation in the yeast display format that produced the original training data for many of these models.
Validate BoltzGen designs against your modified target
The Binder Pilot is a single-round, fixed-scope de novo binder campaign. Bring a BoltzGen candidate pool, we sort against your real glycosylated or modified protein in yeast display, return ranked hits with a technical report. Scoped for academic labs, seed biotech, industrial SMBs.
See the Binder Pilot → Flagship programMulti-round glycoprotein binder campaign
The AI Binder Sprint runs BoltzGen alongside RFdiffusion and BindCraft over 6 to 8 weeks with milestone check-ins and a 100% binder guarantee. The right scope when the target is hard, the modifications matter, and you need a binder on a deadline.
See the AI Binder Sprint →Design against the target your cells actually express
Create a free tools.ranomics.com account and run BoltzGen on your glycosylated, phosphorylated, or non-canonical target. Refolded results, ranked and ready for handoff.