Antibody structure prediction

What is antibody structure prediction?

Antibody structure prediction is the computational process of building three-dimensional antibody variable domains from heavy- and light-chain sequences. It gives specialized attention to conserved frameworks and diverse complementarity-determining-region loops, with CDR-H3 commonly remaining the most uncertain loop.

Antibody-specialized models use the conserved immunoglobulin fold while learning variable loop geometries that shape antigen recognition. Correct chain pairing and complete variable-domain boundaries are foundational requirements.

Read confidence at residue and loop level. A strong framework does not guarantee an accurate CDR-H3 conformation, and an unbound model can rearrange upon antigen binding.

When to use antibody structure prediction

• Antibody engineering is planned. Use it for structural triage and paratope-focused analysis.
• Paired variable domains are known. Provide correctly paired VH and VL sequences.
• Loop uncertainty can be accommodated. Treat CDR predictions as hypotheses for validation.

Benefits of antibody structure prediction

• Antibody-specialized model families are used. They focus on immune-receptor geometry.
• Paired-chain context is retained. Framework packing can be evaluated in the correct pairing.
• Loop uncertainty is visible. Confidence supports targeted review of CDRs.

Primary limitations

• CDR-H3 remains difficult. This diverse loop often has the greatest uncertainty.
• Incorrect pairing invalidates the model. Heavy and light identity must be confirmed.
• Binding can change loops. An unbound structure may not represent the paratope state.

Antibody-model interpretation

ABodyBuilder3 and ImmuneBuilder are trained for immune-receptor geometry rather than general proteins. Compare framework alignment and each CDR separately; numbering with ANARCI can map equivalent positions, while surface tools contextualize rather than validate coordinates.

How to run antibody structure prediction online

1. Verify chain identity. Confirm correctly paired heavy and light chains and variable-domain boundaries.
2. Provide VH and VL separately. Preserve the intended pairing in the workflow inputs.
3. Run antibody models. Use ABodyBuilder3 and ImmuneBuilder with documented settings.
4. Inspect CDR confidence. Compare framework superposition, loop geometry, and residue-level confidence.
5. Validate downstream. Export both structures and use antigen-bound data, mutagenesis, or experimental structures.

How antibody structure prediction works

Send paired VH and VL sequences to ABodyBuilder3 and ImmuneBuilder and retain both structures and confidence estimates.

1. Verify chain identity. Confirm the heavy and light sequences are correctly paired and cover the intended variable domains.
2. Trim variable domains. Provide VH and VL as separate workflow inputs.
3. Run antibody models. Run ABodyBuilder3 and ImmuneBuilder with their documented model settings.
4. Inspect CDR confidence. Compare framework superposition, CDR-loop geometry, and residue-level confidence.
5. Prepare downstream validation. Export both structures and use antigen-bound data, mutagenesis, or experimental structures for validation.

Inputs and outputs

Check formats before running, then inspect and download the result from every workflow step.

Inputs

Research input

FASTAPDBmmCIF

Paired VH and VL amino-acid sequences with correct variable-domain boundaries.

Outputs

Prediction and review outputs

PDBmmCIFCSVJSON

Predicted antibody PDB structures with model-native per-residue confidence or error estimates.