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Design De Novo Antibodies

RFantibody, from the IPD’s Baker Lab, designs antibodies, VHHs, and scFvs starting from a target structure and a desired epitope.

If you want to design CDRs of an antibody/nanobody and you have a known target structure, we recommend trying RFantibody!

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Experimental Validation

The authors use cryo-EM and to demonstrate the folding of the generated structures into Igs at the intended binding site.

The authors show a validated, influenza-targeting VHH, along with scFvs (for both light and heavy chains) to TcdB and a Phox2b peptide-MHC complex. The authors perform affinity optimization after the initial round of generated binders.

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Methods

The RFantibody pipeline has three steps:

  1. Rfantibody (Structure Generation): Finetuned RFdiffusion model which generates structure without amino acids assigned to the newly designed CDR loops (you’ll notice poly-Gs in your RFantibody outputs)
  2. ProteinMPNN (Sequence Design): design of CDR sequences with ProteinMPNN: Find sequences which fold into the RFantibody generated structures
  3. RF2 (Design Validation): A fine-tuned RoseTTAFold2 to evaluate if the designed sequences fold into their initial structures

On Tamarind, the entire pipeline to be run with our RFantibody tool, just provide an input target, framework, and epitope residues.

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Filtering Methods

The authors recommend filtering by: RF2 pAE < 10 and RMSD (design vs. RF2 predicted) < 2Å

These serve as minimums and there isn’t a strong guarantee that sequences conforming to the standards are binders The most significant bottleneck to this pipeline being adopted is the quality of the filtration step.

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Scale

The smallest number of designs the authors find hits for is a set of 95 VHHs. They also note that a more realistic number of designs to test may be in the 10k range. This is primarily because RF2 is not a very strong filter for non-binders and binders, and finding a stricter filter remains a challenge. For the time being, yeast display or other high throughput screening methods seem to be the most reliable path to identifying de novo binders.

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Target Site and Hotspot Selection

A site candidate for binding should have at least 3 hydrophobic residues, as binding to polar sites and those with nearby glycans remain a challenge. See here for strategies on how to generate binders to unstructured peptides, which should be applicable to unstructured loops as well.

RFantibody is highly sensitive to specified epitope/hotspots. We recommend trying a smaller number of designs to see if the generated poses match your expectations. Once you have set of settings you are happy with Tamarind can support tens of thousands of in silico designs.

Try RFantibody