Abstract
Abstract
ABSTRACT Cancer‐associated cachexia is a devastating syndrome characterized by progressive weight loss, reduced survival, and impaired responses to anticancer therapies. Growth differentiation factor 15 (GDF15), acting through its receptor GFRAL, has emerged as a key mediator of cachexia, yet effective and mechanistically defined strategies to neutralize this pathway remain limited. Here, we applied structure‐guided de novo protein design to generate compact minibinders that selectively target the GDF15–GFRAL interaction interface. Using an integrated computational pipeline combining RFdiffusion, ProteinMPNN, and AlphaFold 3 structure prediction, we designed and experimentally validated high‐affinity GDF15 minibinders with picomolar‐range binding affinities and exceptional structural stability. Mutagenesis and charge‐complementary rescue experiments confirm that these minibinders neutralize GDF15 through precisely engineered interface contacts. Functionally, the minibinders suppress GDF15–GFRAL signaling, inhibit downstream transcriptional responses, and robustly reverse cachexia in vivo across multiple tumor models, resulting in significant improvements in body weight and survival. Importantly, neutralization of GDF15 also restores sensitivity to anti–PD‐1 immunotherapy in a GDF15‐driven resistant tumor model. Combination treatment enhances CD8 + T cell infiltration and effector function within tumors, and its antitumor efficacy is strictly dependent on CD8 + T cells. Together, these findings demonstrate that de novo designed GDF15 minibinders can achieve potent, mechanism‐defined neutralization of the GDF15–GFRAL axis in vivo, translating into robust physiological benefits and restoration of immunotherapy efficacy.
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@article{Wang2026Novo,
title = {De Novo Designed Minibinders Targeting the GDF15–GFRAL Axis Reverse Cancer Cachexia and Restore Anti‐Tumor Immunity},
author = {Haitao Wang and Tianzhen Hua and Meiling Wang and Bingbing Meng and Yue Zhang and Chengxu Jiang and Yunhe Gao and Hongqi Yang and Jian Bo},
journal = {Advanced Science},
year = {2026},
doi = {10.1002/advs.76202},
url = {https://doi.org/10.1002/advs.76202}
}
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