Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies

Antibodies are indispensable in biomedicine. However, conventional antibody development faces challenges, including high costs and lengthy production timelines spanning months. Here, we present a data-driven workflow for engineering random heteropolypeptides (RHPs) as synthetic polyclonal antibodies (SpAbs) with programmable binding properties. By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. The SpAbs exhibited binding affinities comparable to natural antibodies, with the top candidate achieving a dissociation constant of 7.9 nM for TNF-α and 418-fold selectivity over human serum albumin, effectively neutralizing TNF-α-induced cytotoxicity. Liquid-phase electron microscopy revealed flexible, intrinsically disordered protein-like conformations and folding-upon-binding dynamics. This study establishes a robust framework for SpAb discovery, demonstrating that sequence-independent RHPs can serve as functional antibody mimics with tunable binding properties, rapid optimization, and broad potential in diagnostics and therapeutics.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2025-06-07
DOI
https://doi.org/10.1021/jacs.5c06240
Citations
4
Primary Topic
Monoclonal and Polyclonal Antibodies Research
Type
article
Field-Weighted Citation Impact
5.44

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article

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies

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Journal of the American Chemical Society
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article

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies

Lijiang Yang, Haisen Zhou, Qing Zhou, Yi Qin Gao, Yuanhao Dai, Guangqi Wu, Huan Wang, Hua Lu, Haolin Chen, Tong Zhao, Zhen Zhu, Zhenghua Zhao, Xiaodong Jing, Wei Ji, Z Y Zhang, Xuehai Yan
article en
4 citations

Abstract

Antibodies are indispensable in biomedicine. However, conventional antibody development faces challenges, including high costs and lengthy production timelines spanning months. Here, we present a data-driven workflow for engineering random heteropolypeptides (RHPs) as synthetic polyclonal antibodies (SpAbs) with programmable binding properties. By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. The SpAbs exhibited binding affinities comparable to natural antibodies, with the top candidate achieving a dissociation constant of 7.9 nM for TNF-α and 418-fold selectivity over human serum albumin, effectively neutralizing TNF-α-induced cytotoxicity. Liquid-phase electron microscopy revealed flexible, intrinsically disordered protein-like conformations and folding-upon-binding dynamics. This study establishes a robust framework for SpAb discovery, demonstrating that sequence-independent RHPs can serve as functional antibody mimics with tunable binding properties, rapid optimization, and broad potential in diagnostics and therapeutics.

Journal of the American Chemical SocietyVol. 147(24)
Chinese Academy of Sciences (CN), Peking University (CN), Beijing National Laboratory for Molecular Sciences (CN), Institute of Process Engineering (CN), Center for Interdisciplinary Studies (IN), Imaging Center (US)
National Natural Science Foundation of China, Chinese Academy of Sciences, Beijing Municipal Science and Technology Commission, Beijing Municipal Natural Science Foundation
Openalex Percentile: Top 4%
Monoclonal and Polyclonal Antibodies Research
5.44
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