Computational De Novo Design of Miniprotein Candidate Neutralizers Targeting the IL-23 Cytokine Subunit p19

Raghav Paladugu, Fardina Fathmiul Alam

Interleukin-23 (IL-23) drives T-helper-17 inflammation underlying inflammatory bowel disease and psoriasis; anti-p19 antibodies are clinically effective but large, costly, and injectable. De novo miniproteins are an attractive alternative, yet published designs target the IL-23 receptor; the IL-23 cytokine itself has not been addressed. A further obstacle is that the standard confidence score (ipTM) tracks construct length rather than interface quality. We computationally design miniprotein binders against the IL-23R-binding face of p19 (PDB 3DUH) and score every design with ipSAE, a length-robust interface metric derived from AlphaFold-initial-guess predicted aligned error. A conventional RFdiffusion/ProteinMPNN/ColabFold pipeline yields no reliable predicted interface across 972 designs (best ipSAE 0.30), and a scoring artifact, in which single-sequence ColabFold under-rates binder pLDDT by approximately 56 points, explains an earlier misleading "binders do not fold" signal. An interface-optimized hallucination method (BindCraft), scored identically, yields 42 of 50 designs above ipSAE 0.60 (best 0.804). An AlphaFold-independent geometric check reproduces the conventional…