IgGM
Upload your antigen and an antibody or nanobody, then redesign its binding loops, humanise its framework, raise its affinity, or just predict how the two dock — all aimed at the epitope you name.
What it is for
You have an antibody or nanobody and an antigen, and you want to redesign its binding loops, humanise its framework, raise its affinity, or just see how the two dock — one model does all of it, aimed at the epitope you name. For a nanobody from scratch use RFantibody; for a paired heavy and light antibody fragment use ESMFold2 design.
Takes an antibody or nanobody and an antigen and does whatever you need to the pair: redesign the binding loops (the CDRs), rebuild or humanise the framework around them, or simply predict how the two dock. All of it is aimed at the epitope you name, and all of it comes out of one model rather than a chain of them. IgGM, Wang et al., ICLR 2025.
When it fits:
- You already have an antibody or nanobody and want its binding loops, or its framework, rebuilt against a specific antigen and epitope.
- You want to humanise a framework, or rebuild it against a new antigen.
- You want to see how an antibody sits on its antigen before committing to a wet-lab project.
Inputs
You will need:
- Antigen structure as PDB or mmCIF (the antigen sequence is read from this file — you do not type it).
- Antibody heavy chain sequence (>H); light chain (>L) optional (omit it for a nanobody / VHH). Mark positions to design with X.
- An epitope: the antigen residues the antibody should aim at. Required for every mode — tools/iggm/__init__.py rejects a run without one.
Each run uses a preset that sets the scale and scope:
- Antibody-antigen complex prediction
- Fold the antibody-antigen complex from the full heavy (and light) chain sequences against your antigen. Provide the epitope residues to guide docking. Fastest mode.
- CDR design (H3 or all CDRs)
- Mask CDR positions with X in the antibody FASTA and IgGM redesigns them against your antigen, aimed at the epitope residues you specify.
- Framework redesign / humanization
- Mask framework positions with X; IgGM redesigns them. Use for humanization or framework engineering.
Parameters you set on the form:
- Antibody FASTA
- Paste the heavy chain as
>Hand, for a conventional antibody, the light chain as>L. Mark residues to design withX. Omit>Lfor a nanobody / VHH. Do not include an antigen record — it comes from the uploaded PDB. - Antigen PDB
- Upload the target as .pdb / .cif. The antigen sequence is extracted from the chain you select, so the structure is the single source of truth.
- Antigen chain
- The chain ID in the uploaded PDB that IgGM should treat as the antigen (e.g.
A). - Epitope
- Required. The antigen residues IgGM should aim the design at; it cannot infer them from an antigen-only structure. Positions are handled correctly regardless of the PDB's residue numbering.
- Mode
- Complex prediction folds the complex; CDR design / framework redesign redesign masked (X) positions.
How to read the results
Per design: the predicted antibody-antigen complex PDB, the designed sequence, and an epitope-contact count (how many of your chosen epitope residues the designed antibody engages). Sequence statistics and amino-acid distribution plots are attached as artifacts.
Where a tool reports them, the scores mean:
- ipTM
- Predicted confidence in the contact between two chains, on a 0 to 1 scale. Higher is better: 0.65 to 0.75 depending on the tool. Individual tools set their own pass bar a little either side of that. On a multi-chain target the number may cover the target's own chain–chain interface as well as the binder's, so read the per-tool note on the results table before comparing designs on it.
- pLDDT
- Per-residue confidence in the predicted fold. Higher means the model is more sure of that part of the structure.
- i_pAE and pAE
- Predicted alignment error, at the interface (i_pAE) or across the whole structure (pAE). Lower is better.
References
Wang et al., ICLR 2025