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Peptide agonist design from GPCR sequences

This article is a preprint and has not been certified by peer review.

Authors

    Binbin Xia,  
    Binbin Xia
    Xingzheng Wang,  
    Xingzheng Wang
    • Institute of Microbiology, Chinese Academy of Sciences
    Qiuxiang Tan,  
    Qiuxiang Tan
    • Shanghai Institute of Materia Medica, Chinese Academy of Sciences
    Gan Xi,  
    Gan Xi
    • Institute of Microbiology, Chinese Academy of Sciences
    Yixin Niu,  
    Yixin Niu
    • Institute of Microbiology, Chinese Academy of Sciences
    Junhao Zhu,  
    Junhao Zhu
    • Institute of Microbiology, Chinese Academy of Sciences
    Yanjie Li,  
    Yanjie Li
    • Institute of Microbiology, Chinese Academy of Sciences
    Rui Ma,  
    Rui Ma
    • Institute of Microbiology, Chinese Academy of Sciences
    Qiang Zhao,  
    Qiang Zhao
    • Shanghai Institute of Materia Medica, Chinese Academy of Sciences
    Longchao Liu,  
    Longchao Liu
    • Institute of Microbiology, Chinese Academy of Sciences
    Jun Wang
    Jun Wang
    • Institute of Microbiology, Chinese Academy of Sciences
Categories
Biology x AI
Keywords
G protein-coupled receptor; Peptide agonist; Protein design

Abstract

Simplifying protein agonists into peptides, or identifying alternative peptide agonists, could substantially enhance the pharmaceutical utility of many GPCR and is therefore highly desirable for biomedical applications. However, de novo peptide design for these receptors remains challenging, as agonism requires both binding to and activation of the targe; such ontarget activity is difficult to incorporate by current approaches that primarily aims at binding. Here we report that based on cross-attention mechanism, our new model GRADE accurately predicts agonist-GPCR pairing using only sequences, and designed peptide agonists across multiple targets with success rates of 20%-40%. Further with the CCR/CXCR family as a focal group, GRADE resulted in agonist with expanded agonistic spectrum than natural chemokines, as well as target-specific agonists. Structural determination revealed GRADE-designed agonists bind to CCR5 orthosteric pocket with novel, reversed polarity of natural chemokines. In a murine model of tumor immune clearance, selected agonists activated CCR5-expressing CD8+ T cells and anti-tumor immunity, in particular the CCR5-specific agonist removed crossreactivity that is inherent to CCL5. With drastically reduced model complexity, GRADE offers an accurate and flexible method for GPCR peptide agonist design using only target sequences, and demonstrates the potential of cross-attention mechanism in future protein ligand design for expanded range of targets.

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DOI:

https://doi.org/10.65215/LTSpreprints.2026.10.15.000361

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361

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2026-10-15

How to Cite

Xia, B., Wang, X., Tan, Q., Xi, G., Niu, Y., Zhu, J., Li, Y., Ma, R., Zhao, Q., Liu, L., & Wang, J. (2026). Peptide agonist design from GPCR sequences. LangTaoSha Preprint Server. https://doi.org/10.65215/LTSpreprints.2026.10.15.000361

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