Deimmunization

DEIMMUNIZATION

As proof-of-concept, Green Fluorescent Protein (GFP) was redesigned to deimmunize. GFP, shown in blue, with immunodominant epitopes of shown in yellow. Mutations in green were selected by ML-guided, Rosetta-based epitope redesign. As shown in Removing T-cell epitopes with computational protein design. King C, et al. Proc Natl Acad Sci U S A. 2014

Deimmunization

An example of protein design: Immunogenicity – a challenge for Biologics

For therapeutic reasons, patients may be exposed to a protein they have had no prior exposure to. They may have inherited a damaged or missing gene such that they do not produce the protein naturally and are being provided with a replacement protein as therapy. Or they may suffer from an illness that can be effectively treated with a non-human protein.

Perceived as foreign, patients mount an immune response and anti-drug antibodies (ADAs) bind to and clear the foreign protein. In some cases, the immune response may be strong enough that the patient experiences an allergic reaction – a mild infusion-site reaction, but possibly anaphylaxis and cardiac arrest. More commonly, the ADAs simply cause the drug to lose efficacy. About a quarter of all approved protein biologics are known to have measurable immunogenicity (>5% of patients with signs of an immune response).

T-cell Epitopes

T-cell epitopes – the regions of a protein that can trigger these immune responses via the MHC complex – can be identified both through empirical in vitro assays and via computational prediction. The rational modification of these epitopes has been difficult, because of the complexity of protein structure. Using our unique platform, Cyrus can use software to redesign and rebuild proteins, removing immune epitopes while maintaining key structural features for the protein’s normal function.

To replace an immune epitope of 15 amino acids requires the evaluation of up to 10¹⁹ different amino acid combinations, an enormous number and something clearly impossible to tackle experimentally. Most of these substitutions will destabilize the protein and prove useless. That’s where a computational approach combining Rosetta with ML comes into play. Because our algorithms permit the simultaneous evaluation of more than 10⁹ substitutions in hours, identifying productive replacements becomes a tractable problem.

Cyrus combines Rosetta and ML-based epitope predictions for deimmunization. Rosetta is excellent for selecting mutations that retain structure/function and epitope predictions will bias mutation-selection towards favorable mutations that are not predicted to have an immune response. This allows Cyrus to develop proteins that are less likely to trigger ADAs while retaining therapeutic function.