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

Confidence should be read at residue and loop level. A strong framework does not guarantee an accurate CDR-H3 conformation, and an unbound antibody model may rearrange when it binds antigen.

When to use antibody structure prediction

• Suitable antibody structure prediction question. Antibody engineering, structural triage, paratope analysis, and downstream complex modeling
• Inputs and evidence are available. Correctly paired variable-domain heavy and light chain sequences
• A validation plan is in place. Assess each CDR independently and avoid treating high framework confidence as evidence for the antigen-bound paratope conformation.

Benefits of antibody structure prediction

• Uses antibody-specialized model families.
• Preserves paired-chain context.
• Provides loop-level uncertainty for review.

Primary limitations

• CDR-H3 remains difficult.
• Incorrect pairing invalidates the model.
• Antigen binding can change loop conformations.

Antibody structure prediction methods and interpretation

ABodyBuilder3 and ImmuneBuilder are trained for immune-receptor geometry rather than general proteins. Their specialized priors improve speed and focus, but predictions still depend on chain identity, numbering, and loop diversity.

Compare framework alignment and each CDR separately. Numbering with ANARCI can help map equivalent positions, while surface and paratope tools can contextualize a model without validating its coordinates.

How to run antibody structure prediction online

The ProteinIQ workflow keeps the inputs, actual tool runs, method-native files, and comparison outputs together. Follow these steps while preserving the scientific boundary described above.

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.

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

Outputs

• Prediction and review outputs. PDB mmCIF CSV JSON Predicted antibody PDB structures with model-native per-residue confidence or error estimates.