Protein engineering
Antibody design
Generate and redesign antibodies or nanobodies with antigen, framework, CDR, structure, and developability context.
Inputs
2 required
Methods
4 connected
- 01RFantibody
- 02mBER
- 03BoltzGen Nanobody
- 04BoltzGen Fab
Compare RFantibody, mBER, and BoltzGen designs from antigen and framework structures.
Use this templateWhat 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
- Best fit. Generating antigen-specific antibodies, Fabs, nanobodies, or redesigned CDRs
- Required starting evidence. An antigen structure, framework or antibody context, intended format, and binding assays
Benefits of antibody design
- Focused search. Uses antibody-specialized representations
- Connected evidence. Supports multiple therapeutic formats
- Testable candidates. Connects binding and developability review
Primary limitations
- Model scope. Antigen structures may omit relevant states
- Score uncertainty. Predicted affinity is not measured affinity
- Experimental requirement. Immunogenicity cannot be eliminated computationally
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.
How to run antibody design online
Use the workflow as an inspectable computational funnel. Preserve the native output of each method, apply explicit acceptance gates, and keep the evidence behind every selected and rejected candidate.
- Prepare antigen. Prepare the antigen structure and define the intended epitope, biological assembly, and relevant glycans.
- Choose format. Choose antibody, Fab, or nanobody format and supply a compatible framework where required.
- Generate candidates. Generate diverse candidates while retaining chain pairing, numbering, settings, and model outputs.
- Review structures. Inspect CDR geometry, predicted interfaces, clashes, liabilities, humanness, and developability.
- Test binding. Express selected candidates and measure affinity, specificity, competition, and functional activity.
How to evaluate 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.
Export structures, sequences, settings, scores, logs, and selection criteria together. A reproducible handoff makes computational assumptions visible to the team planning synthesis, expression, biophysical characterization, and functional assays.
How antibody design works
Compare RFantibody, mBER, and BoltzGen designs from antigen and framework structures.
- Prepare antigen. Prepare the antigen structure and define the intended epitope, biological assembly, and relevant glycans.
- Choose format. Choose antibody, Fab, or nanobody format and supply a compatible framework where required.
- Generate candidates. Generate diverse candidates while retaining chain pairing, numbering, settings, and model outputs.
- Review structures. Inspect CDR geometry, predicted interfaces, clashes, liabilities, humanness, and developability.
- 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.
PDBFASTAJSONTXTAn antigen PDB and, when required, an antibody framework or existing variable-domain structure.
Outputs
- Design and review outputs.
PDBFASTACSVJSONPaired antibody or nanobody sequences, complex models, rankings, interface evidence, and developability results.
Tools for antibody design
Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.
RFantibody
Generate framework-aware antibody designs
mBER
Generate antigen-conditioned antibody binders
BoltzGen
Generate Fab and nanobody candidates
IgDesign
Redesign antibody sequences and CDRs
AntiFold
Run antibody-specialized inverse folding
DiffAb
Design antibody sequence and structure variants
BioPhi
Assess humanness and sequence liabilities
ABodyBuilder3
Predict antibody variable-domain structures
ParaSurf
Predict antibody paratope regions
MolProbity
Review clashes and stereochemical geometry
NetSolP-1.0
Estimate sequence-level solubility
Aggrescan3D
Inspect structure-based aggregation-prone regions
Other protein engineering workflows
Compare related approaches based on the molecular system, available evidence, required inputs, and decision you need to support.
De novo protein design
Generates new protein backbones and sequences rather than modifying a supplied natural template.
Inverse folding
Searches for amino-acid sequences expected to adopt a supplied three-dimensional backbone.
Enzyme design
Designs catalytic scaffolds and ligand-aware sequences around active-site geometry.
Peptide design
Generates short peptide sequences for binding or other desired molecular properties.
Protein sequence design
Creates or optimizes amino-acid sequences against structural, functional, or developability goals.
Protein binder design
Designs proteins intended to recognize a specified target surface or epitope.
Frequently asked questions
Choose the format required by the biological mechanism and downstream assay. Nanobodies offer compact single-domain scaffolds, while Fabs and paired antibodies preserve heavy–light interfaces and may better match the intended therapeutic format.
Use the biologically relevant state with the intended oligomer, domain boundaries, glycans, cofactors, and membrane context when available. Compare alternate conformations if epitope accessibility changes between states.
Preserve framework positions that support packing, canonical loop geometry, pairing, and known developability unless there is evidence to redesign them. Clearly separate framework mutations from CDR mutations during review.
Cluster candidates by paired CDR sequence and predicted paratope geometry, then select across clusters. Also track framework identity so apparent CDR diversity is not confounded by incompatible or repeated scaffolds.
Published full antibody-discovery prices span materially different packages. DuneX lists VHH or scFv campaigns at $5,000 for a pilot, up to $15,000 for its Standard package when the success milestone is met, and up to $25,000 for Premium; Invenra lists a $100,000 full mAb discovery and data package under its 2026 partnership offer.
Format, antigen readiness, library breadth, epitope and counter-selection requirements, affinity targets, clone count, expression, developability work, functional assays, optimization rounds, ownership terms, and success fees determine which package is comparable.
ProteinIQ self-service starts at $29 per month for academic Plus and $99 per month for commercial Pro, with the configured run quoted in credits before submission. A done-for-you antibody design project is scoped separately; synthesis, expression, and experimental assays are included only when the project quote explicitly says so.
Start with a workflow you can inspect and edit
Add your inputs, review the settings, and keep every structure, score, table, and file connected to the step that produced it.