Abstract
<jats:p>IL-4Ra is a key regulatory receptor for type 2 inflammatory responses, signal transduce from IL-4 and IL-13 through binding with IL-13Ra or the gamma c chain to activate the downstream JAK1-STAT6 pathway. IL-4Ra is currently the most successful "golden target" in the field of allergic disease therapeutics. Its representative monoclonal antibody drug, dupilumab, through the dual blockade mechanism of IL-4/IL-13 has pioneered a new era of precision therapy for type 2 inflammation. In our manuscript, we employed large-scale deep learning-based computational design methods to de novo design mini-protein antagonists specific for both human and mouse IL-4Ra. The binding affinity was improved from 22.1 nM to 569 pM through partial diffusion. The design accuracy and binding specificity were verified through X-ray crystallography and biochemical studies. In vitro IL4/IL13 signal blockade assays revealed that de novo designed monomeric mini-protein antagonist exhibited comparable blockade ability to bivalent dupilumab. In vivo pharmacokinetic half-life studies demonstrated that fusion to an HSA-binding domain extended the half-life of the mini-protein antagonist from 2.7 hours to 60.6 hours. The IL-4Ra mini-protein antagonist had excellent expression levels, solubility and thermal stability. The IL4/IL13 signal blockade ability remained unchanged even after being heating to 95 degree. In conclusion, through large-scale cluster computing and deep learning-based de novo design, we developed well-performed IL-4Ra mini-protein antagonist, and demonstrates certain potential for drug development.</jats:p>