RFantibody

What is RFantibody?

RFantibody is a structure-guided antibody and nanobody design pipeline built around an antibody-tuned version of RFdiffusion. It generates new CDR-mediated binding geometries against a chosen epitope, designs compatible loop sequences, and then filters the resulting complexes with an antibody-tuned RoseTTAFold2 model.

The method was introduced as a route to de novo, epitope-specific antibody design from structural input rather than immunization or random library discovery. In the published workflow, the computational design stage is followed by experimental screening, because the model can generate structurally accurate binders but still requires broad sampling to recover the strongest candidates.

How to use RFantibody online

ProteinIQ runs RFantibody on hosted GPU infrastructure, so the full three-stage pipeline can be launched from the browser without installing RosettaCommons dependencies locally.

The online interface accepts one target structure and one antibody or nanobody framework. The target can be uploaded as a PDB/ENT file or fetched from the RCSB. The framework must already be prepared in RFantibody's HLT format.

Inputs

HLT framework requirements

Generic antibody PDB files usually do not include those loop remarks or chain conventions. In the native RFantibody workflow, such structures are converted to HLT format before design.

Design settings

CDR loop ranges

At least one loop must be enabled. To keep a loop fixed, turn off its design toggle. A blank length on an enabled loop does not skip the loop; it uses the native length syntax such as H1:.

Targeting

Advanced settings

Outputs

How does RFantibody work?

RFantibody runs three design stages in sequence.

Antibody-tuned RFdiffusion

The first stage places a chosen framework against the target and redesigns the selected CDR loops. Rather than generating an arbitrary binder scaffold from scratch, this stage treats the framework as fixed context and focuses the generative step on loop-mediated docking and backbone creation around the target epitope.

ProteinMPNN sequence design

Once loop backbones have been generated, ProteinMPNN samples amino acid sequences compatible with each designed structure. In RFantibody, this step is used to populate the redesigned loops while preserving the broader framework context.

Antibody-tuned RF2 filtering

The final stage predicts the structures of the designed sequences in complex with the target and scores whether the intended interface appears self-consistent. This filtering stage is important because antibody design campaigns generally require broad sampling, and only a subset of computational designs retain the desired dock after structure prediction.

Interpreting results

RFantibody output is best treated as a candidate set rather than a final answer. Structural inspection still matters: plausible designs should maintain a well-packed interface, avoid obvious clashes, and keep the framework geometry reasonable outside the redesigned loops.

RFantibody's guidance recommends filtering designs with strong RF2 confidence and low disagreement between the design model and the RF2-predicted complex. In practice, RF2 pAE below roughly 10 Å and design-versus-prediction Cα RMSD below roughly 2 Å are used as minimal screening criteria before downstream experimental work or more detailed physics-based evaluation. For hotspot-guided runs, smaller mindist and averagemin values indicate that the generated CDR geometry landed closer to the requested epitope. Candidate numbers record generation order; they are not a score-based ranking.

Applications

• Epitope-specific nanobody design: Generating VHH binders against structurally defined target surfaces.
• Antibody variable-domain design: Designing scFv-like variable regions with both heavy- and light-chain CDR participation.
• Targeted campaign exploration: Running pilot campaigns to compare hotspot definitions, loop ranges, or framework choices before large-scale screening.