<b>Discovering Cell-Targeting Ligands and Cell Surface Receptors by Selection of DNA-Encoded Chemical Libraries against Cancer Cells without Predefined Targets</b>

Gui, Yuhan;Hou, Rui;Yuchen, Huang;Liu, Shihao;Meng, Ling;Li, Ying;Lam, Fong Sang;Ding, Ruoyun;Cao, Yan;Li, Gang;Lu, Xiaojie;Li, Xiaoyu

Description

Small molecules that can bind to specific cells have broad applications in cancer diagnosis and treatment. Screening large chemical libraries against live cells is an effective strategy for discovering cell-targeting ligands. The DNA-encoded chemical library (DEL or DECL) technology has emerged as a robust tool in drug discovery and has been successfully utilized in identifying ligands for biological targets. However, nearly all DEL selections have predefined targets, while target-agnostic DEL selections interrogating the entire cell surface remain underexplored. Herein, we systematically optimized a cell-based DEL selection method against cancer cells without predefined targets. A 104.96-million-member DEL was selected against MDA-MB-231 and MCF-7 breast cancer cells, representing high and low metastatic properties, respectively, which led to the identification of cell-specific small molecules. We further demonstrated cell-targeting applications of these ligands in cancer photodynamic therapy and targeted drug delivery. Finally, leveraging the DNA tag of DEL compounds, we identified α-enolase (ENO1) as the cell surface receptor of one of the ligands targeting the more aggressive MDA-MB-231 cells. Overall, this work offers an efficient approach for discovering cell-targeting small molecule ligands by using DELs and demonstrates that DELs can be a useful tool to identify specific surface receptors on cancer cells.

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Metrics

Dataset Index

0.5

FAIR Score

88%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

HKU Data Repository

License

Creative Commons Attribution Non Commercial 4.0 International

Assigned Domain

Subfield

Immunology and Allergy

Field

Medicine

Domain

Health Sciences

Confidence Score

59%

Source

Scholar Data Model

Keywords

Biochemistry and cell biology not elsewhere classifiedMolecular medicineProteins and peptides

Normalization Factors

FT

65.38

CTw

1.00

MTw

1.00