ABodyBuilder3

Predict paired antibody variable-domain structures from heavy and light chain sequences.

Input

Heavy chain (VH)A

Light chain (VL)B

Model checkpoint

50 credits

Output

Configure inputs to begin

Set options on the left, then click “Submit job”.

What is ABodyBuilder3?

ABodyBuilder3 predicts the 3D structure of an antibody variable domain (Fv) from its paired heavy and light chain sequences. It is the third generation of the ABodyBuilder line from the Oxford Protein Informatics Group, built on the ImmuneBuilder framework, and it specializes in the part of antibody modeling that general structure predictors handle worst: the hypervariable CDR loops, especially CDR-H3.

Most of an antibody Fv is conformationally conserved, so the framework regions are easy to model. The binding loops are the hard part. CDR-H3 has no canonical templates, varies in length and sequence across antibodies, and largely determines where and how the antibody binds. ABodyBuilder3 focuses its accuracy there.

How to use ABodyBuilder3 online

Paste an antibody heavy chain (VH) and light chain (VL) sequence into ProteinIQ, pick a model checkpoint, and run the prediction on cloud GPUs. The result is a single Fv structure in PDB format with heavy and light written as separate chains, plus a summary of chain lengths and, on the pLDDT checkpoint, per-residue confidence. No installation, no antibody numbering setup, and no MSA step.

Inputs

Input	Format	Notes
`Heavy chain (VH)`	Amino-acid sequence or FASTA	Standard 20-letter alphabet. Provide the variable domain only, not the full heavy chain.
`Light chain (VL)`	Amino-acid sequence or FASTA	Required. ABodyBuilder3 models paired Fv, so both chains are needed.

Both inputs expect the variable domain sequence. Trimming constant-region residues before submitting avoids modeling regions the tool is not trained on. To assign and trim variable domains by antibody numbering, use ANARCI.

Settings

Setting	Default	Description
`Model checkpoint`	`ABodyBuilder3-LM loss checkpoint`	Selects which trained model runs. The LM checkpoint uses protein language model embeddings for residue representation. The pLDDT checkpoint additionally predicts per-residue confidence.

Two checkpoints are available because the paper trains the model two ways:

ABodyBuilder3-LM loss checkpoint: the default. Selected on validation loss, it gives the most accurate backbone for routine structure prediction.
ABodyBuilder3 pLDDT checkpoint: trained with a confidence head that emits per-residue pLDDT. Choose this when the confidence values matter, for example to flag an unreliable CDR-H3 loop before downstream docking.

Results

Output	Format	Notes
Predicted Fv structure	PDB	Heavy and light domains as separate chains.
pLDDT data	JSON	Returned only on the pLDDT checkpoint.
Summary table	Spreadsheet	Heavy and light chain lengths, total residues, model checkpoint, and mean pLDDT when available.

How ABodyBuilder3 works

ABodyBuilder3 inherits the ImmuneBuilder architecture: a graph-based network predicts inter-residue geometry, which is folded into atomic coordinates and refined. The two changes that define this version are the sequence representation and the confidence estimate.

Language model embeddings

ABodyBuilder2 encoded each residue as a one-hot vector. ABodyBuilder3 replaces that with embeddings from the ProtT5 protein language model, which has seen billions of natural protein sequences and carries evolutionary and structural context that a one-hot vector cannot. This richer representation stabilizes training and improves modeling of the variable loops, with the largest effect on CDR-H3 and CDR-L3. The gain on CDR-H3 backbone RMSD is real but modest; the more reliable benefit is more robust training and a model that no longer needs an ensemble to estimate confidence.

pLDDT confidence instead of an ensemble

Earlier ImmuneBuilder models estimated per-residue uncertainty by training an ensemble of networks and measuring their disagreement, which multiplies both training and inference cost. ABodyBuilder3 adds a pLDDT head that predicts local accuracy directly, in the same 0 to 100 scale used by AlphaFold. A single model with this head matches the ensemble's uncertainty estimate at a fraction of the compute.

Interpreting pLDDT

pLDDT	Meaning
> 90	High confidence, backbone very likely correct.
70 to 90	Reliable, framework and well-defined loops.
50 to 70	Treat with caution, common in long or unusual CDR-H3 loops.
< 50	Low confidence, the local geometry may be wrong.

Confidence tends to be high across the framework and lower through the CDRs. A low score concentrated in CDR-H3 is expected for long or atypical loops and is the signal to validate that region before relying on it.

When to use ABodyBuilder3 vs alternatives

ABodyBuilder3 is the right choice when the target is an antibody or nanobody Fv and the goal is a fast, accurate model of the binding loops. It is purpose-built for that, runs in seconds, and needs no MSA.

For a full antigen-antibody complex rather than the unbound Fv, a co-folding model such as Chai-1 or Protenix predicts antibody and antigen together. For general (non-antibody) single-chain prediction, use ESMFold or AlphaFold 2. To redesign CDR sequences or graft new loops onto a predicted scaffold, pair the structure with AntiFold, Proteo-R1, or the antibody-specialized RFantibody and DiffAb.

A typical workflow numbers and trims the chains with ANARCI, predicts the Fv here, then docks against an antigen or feeds the structure into a design step. To compare a prediction against a crystal structure or another model, measure backbone agreement with the RMSD calculator.

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