bioRxivBiochemistryPreprint
AI-Driven Design of Nanobinders Targeting the TSLPR Heterodimer Interface to Suppress Type 2 Inflammatory Signaling
Woo‐Chan AhniD, Min-Jung Son, Jae-Rin Kim, Seong-Ryeong GoiD, J Y KangiD, Junsoo Park, Younghoon LeeiD, S H Lee, Jae‐Joong KimiD, Juyeon JungiD, Eui‐Jeon WooiD, Young‐Joo JeoniD, K S Park
Abstract
Aberrant thymic stromal lymphopoietin (TSLP) signaling is a central driver of type 2 inflammatory diseases, yet the only approved TSLP-targeted therapy is a 150 kDa monoclonal antibody whose bulky format limits tissue penetration and precludes inhaled delivery. Here, we report an AI-driven framework for designing ultra-compact de novo nanobinders that suppress TSLP signaling by sterically disrupting assembly of the TSLPR-IL-7R heterodimer. We compare two structure-guided strategies, namely purely de novo helical bundle generation and interface-mimetic grafting of native binding motifs onto designed scaffolds. Although both yield nanomolar binders, only orthosteric mimicry of the native cytokine geometry blocks receptor heterodimerization, showing that functional antagonism is governed by precise epitope geometry rather than affinity alone. After library-based maturation, the lead nanobinder TRB5.1 is a hyper-stable monomer (Tm [~]97.4 {degrees}C) with single-digit nanomolar affinity (KD = 9.7 nM) and strict selectivity over related -chain interleukin receptors. TRB5.1 suppresses TSLP-induced JAK1 and STAT5 phosphorylation across multiple cellular models and drives a transcriptome-wide reversal of the pathogenic type 2 program. This work delivers a developable, potentially inhalable non-antibody lead and a scalable blueprint for antagonizing heterodimeric cytokine receptors.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- Citations
- 0
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