Antibody design

What is antibody design?

Antibody design is the process of creating or modifying immunoglobulin sequences and structures to achieve desired antigen recognition and molecular properties. Workflows may create binders from an antigen structure, redesign complementarity-determining regions on a framework, or optimize an existing antibody. Antibody-specific numbering, paired chains, loop geometry, germline context, developability, and immunogenicity make this a distinct protein-design task.

The starting point determines the method. De novo antigen-conditioned models can propose antibodies, Fabs, or nanobodies; inverse-folding methods redesign sequences for an existing antibody structure; and CDR-focused methods preserve more of the framework while changing selected binding loops.

Predicted complexes and model scores help prioritize candidates but do not establish affinity, specificity, expression, or safety. Keep heavy–light pairing, numbering scheme, framework identity, antigen structure, and designed-region boundaries with every candidate.

When to use antibody design

Choose an antibody-specific route when chain format and antigen context matter:

• An antigen structure supports a proposed epitope.
• A Fab, paired antibody or VHH nanobody format is specified.
• You can test binding and counter-screen specificity.

Strengths and limitations

Antibody-focused methods can preserve framework or chain-format constraints while exploring CDR-mediated binding geometries. Comparing antigen-conditioned generators produces distinct interface hypotheses, and separate humanness or developability checks can flag sequence liabilities before testing.

Antigen structures may omit glycans or relevant conformations, and uncertain loops can dominate a predicted interface. A high model rank does not measure affinity, specificity or neutralization. Computational humanness and immunogenicity estimates cannot eliminate immune risk, so candidates need experimental characterization.

Antibody design methods

Antigen-conditioned generators propose antibody structures or sequences around a target surface. Framework-aware and CDR-redesign methods constrain more of the molecule, which can reduce search space while preserving known biophysical properties.

Antibody inverse folding and humanness models answer different questions. A sequence can be structurally compatible yet carry developability or immune-risk liabilities, so those assessments should remain separate.

Applications of antibody design

Antigen-conditioned design can propose new variable regions for an epitope that lacks a known antibody. Comparing Fab and VHH outputs can reveal format-dependent access to a recessed or crowded surface.

Framework or CDR redesign is useful when an existing antibody already binds but needs changes in recognition or molecular properties. Keep binding and developability as separate questions: a stable-looking model may still bind weakly or nonspecifically.

How to run antibody design online

The featured template runs antibody and nanobody generators in parallel from one antigen structure. The RFantibody branch also uses a framework input.

1. Prepare the antigen. Supply a PDB with the relevant assembly, epitope context and glycans or neighboring chains that affect access.
2. Choose the format. Record whether the intended output is a paired antibody, Fab or VHH, and supply a framework PDB for the RFantibody branch when required.
3. Run independent generators. Use RFantibody for framework-aware designs. Compare its complexes with mBER VHH candidates.
4. Review format-specific models. Inspect BoltzGen nanobody and Fab branches separately; do not merge their native ranks.
5. Check interfaces. Review chain pairing, CDR geometry, epitope accessibility, clashes and sequence liabilities before selecting candidates.
6. Test a diverse set. Measure expression, binding, specificity and functional effect with appropriate controls.

How to interpret antibody design results

Review chain pairing, CDR numbering, loop conformations, epitope contacts, clashes, buried surface, sequence liabilities, humanness, aggregation risk, and agreement across structural models.

Experimental testing should include expression, monodispersity, affinity, specificity, off-target binding, competition or epitope mapping, and a function-relevant assay.

Experimental validation and handoff

Keep antigen and framework structures, numbering scheme, chain pairing, designed regions, model settings, and all candidate sequences. Confirm binding, specificity, and function experimentally.

Retain heavy and light chain pairing, CDR boundaries and the antigen structure for each candidate. Measure affinity, specificity and expression for multiple sequence families rather than one top-ranked complex.

How antibody design works

Compare RFantibody, mBER, and BoltzGen designs from antigen and framework structures.

1. Prepare antigen. Prepare the antigen structure and define the intended epitope, biological assembly, and relevant glycans.
2. Choose format. Choose antibody, Fab, or nanobody format and supply a compatible framework where required.
3. Generate candidates. Generate diverse candidates while retaining chain pairing, numbering, settings, and model outputs.
4. Review structures. Inspect CDR geometry, predicted interfaces, clashes, liabilities, humanness, and developability.
5. Test binding. Express selected candidates and measure affinity, specificity, competition, and functional activity.

Inputs and outputs

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

Inputs

Design input

PDBFASTAJSONTXT

An antigen PDB and, when required, an antibody framework or existing variable-domain structure.

Outputs

Design and review outputs

PDBFASTACSVJSON

Paired antibody or nanobody sequences, complex models, rankings, interface evidence, and developability results.