IgGM

IgGM is a no-install online antibody and nanobody engineering tool you can run through tools.ranomics.com on a dedicated GPU. One model for the antibody work that usually takes four separate ones — loop redesign, humanisation, affinity maturation and predicting the docked complex all run from the same upload.

Design inputs

Mode

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. Affinity maturation Improve binding by exploring variants at the positions you mask with X, against a wild-type reference (same length, no masks). It designs one variant per masked position per sample, so a few samples over a short loop already gives a rich set. Inverse design (sequence from structure) Recover the antibody sequence given the complex backbone.

Antibody FASTA

Paste the heavy chain as >H and, for a conventional antibody, the light chain as >L. Omit >L for a nanobody / VHH. Mark positions to design with X. Do not include an antigen record; it is read from the uploaded PDB.

Antigen PDB

Upload the target as .pdb / .cif. The antigen sequence is read from the chain you select, so the structure is the single source of truth.

Antigen chain

Epitope residues (required)

Required: the antigen residue numbers IgGM should target (space or comma separated, PDB numbering). IgGM designs against a specific epitope and cannot infer one from an antigen-only structure. Get them from Epitope Scout or your own structural analysis. Positions are handled correctly regardless of the PDB's residue numbering.

Advanced

Samples

Max antigen size

Samples: designs to generate (up to 100 total). For affinity maturation, samples are counted per masked position, so samples x masked positions must stay at or under 100 (needs at least 2 samples). Max antigen size: IgGM's memory guard; lower it for very large antigens.

Campaign

Group related runs on your jobs page. Leave blank to file under Uncategorized.

Estimated cost $0.00 based on parameters and historical compute time

Billed by actual GPU consumed, up to the estimate. Surplus returns to your wallet automatically. Cancellation does not refund GPU time already used.

About IgGM

What it is

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, raise affinity from a wild-type starting point, recover a sequence from a structure, 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 to use

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 push affinity up from a wild-type reference.
You want to see how an antibody sits on its antigen before committing to a wet-lab campaign.

Prerequisites

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.
Optional: an epitope — click antigen residues on the structure and IgGM guides design toward them.

Input glossary

Antibody FASTA. Paste the heavy chain as >H and, for a conventional antibody, the light chain as >L. Mark residues to design with X. Omit >L for 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. Optional. Click residues on the antigen structure; IgGM guides design toward them. 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; affinity maturation generates improved variants from a wild-type reference; inverse design recovers sequence from the backbone.

Runtime

Preset	Typical min
complex_prediction	~2 min
cdr_design	~3 min
fr_design	~3 min
affinity_maturation	scales with samples x masked positions
inverse_design	~2 min

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.

References

A guided first run

Before designing anything, check that IgGM can place your existing antibody on your antigen. A single sample in prediction mode is already the smallest run IgGM offers, so these are the form’s own defaults — a guided first run at the tool’s normal cost, not a cheaper trial.

What you need

Your antigen structure file, and your antibody heavy chain sequence. The light chain is optional — omit it for a nanobody.

What it costs

About $0.08, and about ~2 min of GPU time. That is an estimate from these exact settings — you are billed for the compute the job actually uses, never more than the estimate.

Then what

If the predicted complex looks right, switch the mode to CDR design and mark the positions you want redesigned with X.

Load these settings →

What good looks like

Each tool reports a subset of these depending on whether it does design, sequence recovery, or structure prediction.

ipTM: Predicted confidence in the contact between two chains, on a 0 to 1 scale. Higher is better: > 0.75 strong; > 0.65 acceptable. Individual tools set their own pass bar a little either side of that — the guide for the tool you ran states its one. On a multi-chain target the number may cover the target's own chain–chain interface as well as the binder's.
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.
ProteinMPNN recovery: Fraction of native residues recovered when ProteinMPNN redesigns a known sequence on its native backbone — the folded shape on its own, before a sequence is chosen for it. Higher is better: > 0.4 usable; > 0.6 excellent.

Related tools on Ranomics

If you are picking between IgGM and a sibling algorithm, these run on the same hub against the same target.

Run RFantibody online: You have a target structure and want nanobodies — single-domain antibodies you can carry straight into yeast display, mammalian display or a hybridoma workflow. Each candidate is refolded and scored against the target before you see it. For non-antibody mini-proteins use RFdiffusion or BindCraft; for sugar-coated targets use BoltzGen.
Run BoltzGen online: You have a target and have not settled on what shape the binder should be. One model here aims mini-proteins, nanobodies, antibodies or peptides at the same site, so you can compare formats instead of guessing. The only design tool here that handles sugars and modified residues on the target natively.
Run Boltz-2 online: You already have a binder sequence and the target it should hit, and you want to know whether they actually stick together before you order DNA. Returns the predicted complex and a 0-to-1 interface confidence score. Trained on antibody-antigen complexes, so it is a genuinely second opinion next to AlphaFold2.