Successful biotherapeutic discovery to date has relied upon immunodominance - immune systems' prominent responses to only a select few of the many antigenic motifs presented. However, when the mechanism of action of antibody-based drugs requires selective binding to subdominant epitope(s) on the target molecule(s), the constraints of immunodominance limit discovery of promising epitope-selective drug candidates.
Combining propietary machine learning technology with our massively parallel interrogation of diverse molecular libraries in silico and in vitro, RubrYc discovers and deploys Meso-scale Engineered Molecules (MEMs) to enable discovery of subdominant epitope-selective drug candidates.
Our MEM-programmed in vitro selection of subdominant epitope selective antibodies allows RubrYc to expand on-target and on-epitope optionality, reducing the risk of unproductive discovery campaigns.
From Massive to Manageable
RubrYc explores infinite peptide sequence space to define MEMs- the proprietary molecular tools we use to focus discovery campaigns on subdominant epitopes, novel mechanism(s) of action, the classically undruggable target families.
