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Protein-templated ligand discovery via the selection of DNA-encoded dynamic libraries

Nature Chemistry volume 16, pages 543–555 (2024)Cite this article

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Abstract

DNA-encoded chemical libraries (DELs) have become a powerful technology platform in drug discovery. Dual-pharmacophore DELs display two sets of small molecules at the termini of DNA duplexes, thereby enabling the identification of synergistic binders against biological targets, and have been successfully applied in fragment-based ligand discovery and affinity maturation of known ligands. However, dual-pharmacophore DELs identify separate binders that require subsequent linking to obtain the full ligands, which is often challenging. Here we report a protein-templated DEL selection approach that can identify full ligand/inhibitor structures from DNA-encoded dynamic libraries (DEDLs) without the need for subsequent fragment linking. Our approach is based on dynamic DNA hybridization and target-templated in situ ligand synthesis, and it incorporates and encodes the linker structures in the library, along with the building blocks, to be sampled by the target protein. To demonstrate the performance of this method, 4.35-million- and 3.00-million-member DEDLs with different library architectures were prepared, and hit selection was achieved against four therapeutically relevant target proteins.

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Fig. 1: Protein-templated DEDL selection method.
Fig. 2: Model selections with streptavidin.
Fig. 3: Model selections with HIV-1 PR.
Fig. 4: Selection of DEDL-1 against S-protein.
Fig. 5: Selection of DEDL-1 against NSP13.
Fig. 6: Selection of DEDL-2 against PD-L1 and STAT5b.

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Data availability

All data supporting the findings of this study are available within the Article, the associated Source Data files, Supplementary Information figures and Extended Data figures. All data are also available at figshare (https://doi.org/10.6084/m9.figshare.24297505). All the published tools and packages used for data analysis are provided with the paper. The Protein Data Bank references can be accessed at https://www.rcsb.org/ under accession codes 1ZPA (HIV-1 PR), 6VW1 (S-protein/ACE2 complex) and 7NIO (SARS-CoV-2 NSP13 helicase). Source data are provided with this paper.

Code availability

The custom Python scripts for sequencing data analysis have been made freely available for download at GitHub (https://github.com/GAOYingHKU/DEL_data_analysis; https://github.com/GAOYingHKU/DEL_data_analysis/tree/main/additional_script).

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Acknowledgements

This work was supported by grants from the Shenzhen Bay Laboratory, Shenzhen, China (SZBL2020090501008), the Research Grants Council of Hong Kong SAR, China (AoE/P-705/16, 17301118, 17111319, 17303220, 17300321, 17318322, C7005-20G, C7016-22G and 2122-7S04), NSFC of China (21877093, 22222702 and 91953119), GuangDong Basic and Applied Basic Research Foundation (2023A1515010711), the Fundamental Research Funds for the Central Universities (2022CDJQY-001) and Beijing National Laboratory for Molecular Sciences (BNLMS202104). We acknowledge support from the ‘Laboratory for Synthetic Chemistry and Chemical Biology’ under the Health@InnoHK Program and State Key Laboratory of Synthetic Chemistry by Innovation and Technology Commission, Hong Kong SAR, China. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Y. Jia of The University of Hong Kong for assistance with the physiochemical property analysis of the hit compounds.

Author information

Author notes

  1. These authors contributed equally: Yu Zhou, Wenyin Shen.

Authors and Affiliations

  1. Department of Chemistry and State Key Laboratory of Synthetic Chemistry, The University of Hong Kong, Hong Kong SAR, China

    Yu Zhou, Wenyin Shen, Ying Gao, Jianzhao Peng, Qingrong Li, Xueying Wei, Shihao Liu, Fong Sang Lam, Hongzhe Sun & Xiaoyu Li

  2. Laboratory for Synthetic Chemistry and Chemical Biology Limited, Health@InnoHK, Innovation and Technology Commission, Hong Kong SAR, China

    Yu Zhou, Joan Mayol-Llinàs & Xiaoyu Li

  3. Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences; Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China

    Guixian Zhao & Yizhou Li

  4. Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China

    Gang Li

  5. School of Pharmacy, Naval Medical University, Shanghai, China

    Yan Cao

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  1. Yu Zhou
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Contributions

Y.Z., W.S., H.S., Y.C. and X.L. conceived and designed the experiments. Y.Z., W.S., J.P, Q.L., X.W., S.L., F.S.L., J.M.-L. and G.Z. performed the experiments. Y.Z., W.S., Y.G., G.L., Y.L. and X.L. analysed the data. Y.Z., W.S., H.S., Y.C. and X.L. co-wrote the paper.

Corresponding authors

Correspondence to Hongzhe Sun, Yan Cao or Xiaoyu Li.

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Competing interests

The University of Hong Kong has filed two patent applications on the chemical structures of the inhibitors of S-protein (Chinese patent application no. 202310194549.8) and NSP13 helicase (US provisional patent application no. 63/494,329). X.L., H.S., W.S., Y.Z., X.W. and G.L. are the inventors. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Model selections with streptavidin.

a. A model library consisting of DB-DNA and amino-DNA (1:9) was selected against streptavidin, CA-2, or without protein. The linker structures are the same as A-1/B-1. The products were gel-purified, amplified by relay primer bypass PCR, and decoded with Sanger sequencing. b-d. Sanger sequencing results. The colored peaks represent individual bases (green: A; blue: C; red: T; black: G). The bar graph data in the background represent sequencing quality score for each base, which is calculated based on the estimated error probability for the base call as previously described137,138.

Extended Data Fig. 2 Model selections with HIV-1 PR.

a. The model library in Fig. 3e was selected against HIV-1 PR and the selection results were analyzed by Sanger sequencing. b. The same model library was subjected to the selection conditions without protein as a control experiment. The colored peaks represent individual bases (green: A; blue: C; red: T; black: G). The bar graph data in the background represent sequencing quality score for each base, which is calculated based on the estimated error probability for the base call as previously reported137,138.

Extended Data Fig. 3 Two large DEDLs were prepared for the selection.

R1, R2: building blocks; L: linker. The number of the building blocks and linkers in each library are shown. In sub-library A, R1 is installed before L in DEDL-1 and after L in DEDL-2, both using amidation reactions. Both libraries share the same sub-library B containing the same set of R2, which was installed by direct coupling to the respective DNA via amidation reactions. The azide group was synthesized through on-DNA diazo-transfer reactions from the amino group. See the Supplementary Information for details on DNA sequences, building block structures, library synthesis, and characterization data (Supplementary Figs. 14–17).

Extended Data Fig. 4 Docking analysis of the selected S-protein inhibitors.

a. Crystal structure of the S-protein/ACE2 binding complex (PDB: 6VW1). The interface is highlighted in yellow and the residues involved are shown in sticks. b. S-10 (62-39-224, 1,4-isomer) is docked to S-protein and the detailed interactions are highlighted; the small molecule is shown in green and the amino acids are shown yellow. The hydrogen bonds are shown as dashed lines. c. Comparison of the binding sites of S-10, S-18 (the 1,5-isomer), and S-13 (62C-39-224), highlighted in yellow.

Extended Data Fig. 5 Characterization of the hit compounds from NPS13 selection.

a. Gel analysis of the selections. Top panel: after the selection; bottom panel: after relay primer bypass PCR; see complete gel images in Supplementary Figs. 46, 47. The gel analysis was performed three times with independent samples. All experiments were reproduced with similar results. b. NSP13 helicase ATPase activity assay of the compounds (200 µM); n = 3 independent experiments; data are presented as mean values ± s.d. c. Docking analysis using the crystal structure of NSP13 helicase (PDB: 7NIO). The ATP-binding site is enlarged; top panel: interaction map of 73-1-143; bottom panel: overlay of 73-1-143, ATP, and ADP; small molecules are shown as sticks and amino acids are shown yellow.

Source data

Extended Data Fig. 6 Selection against PD-L1 and STAT5b and characterization of the hit compounds.

a. 2D scatter plots of the selection results with PD-L1. x-axis: post-selection sequence count; y-axis: enrichment fold. b. Results of the time-resolved fluorescence energy transfer (TR-FRET) PD-L1/PD-1 binding assay. Compounds were tested at 1 mM. c. Inhibition titration curve of P-3 (219-16-170, 1,4-isomer) to determine the IC50 values. d. 2D scatter plots of the selection results with STAT5b. x-axis: post-selection sequence count; y-axis: enrichment fold. e. Fluorescence polarization assay to determine the inhibition activity against STAT5b/DNA binding; FAM: fluorescein. f. Results of the fluorescence polarization assay; the compounds were tested at 2 mM. Selection gel images are shown in Supplementary Figs. 46, 47. In b, c, and f, n = 3 independent experiments; data are presented as mean values ± s.d.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–47, Notes, detailed experimental procedure, additional discussion and other additional information related to this study.

Reporting Summary

Supplementary Table 1

Complete list of building blocks and DNA sequences of the libraries.

Supplementary Table 2

Source data for Supplementary figures.

Supplementary Data 1

Source data for library selections in Supplementary Figs. 13–34.

Supplementary Data 2

Source data for library selections in Supplementary Figs. 35–39.

Source data

Source Data Fig. 2

Unprocessed gels.

Source Data Fig. 3

Unprocessed gels.

Source Data Fig. 3

Library selection source data.

Source Data Fig. 4

Unprocessed gels.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

Library selection source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Library selection source data.

Source Data Fig. 6

Library selection source data.

Source Data Extended Data Fig. 5

Unprocessed gels.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 6

Library selection source data.

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Zhou, Y., Shen, W., Gao, Y. et al. Protein-templated ligand discovery via the selection of DNA-encoded dynamic libraries. Nat. Chem. 16, 543–555 (2024). https://doi.org/10.1038/s41557-024-01442-y

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