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Selection of DNA-encoded chemical libraries against endogenous membrane proteins on live cells

Nature Chemistry volume 13, pages 77–88 (2021)Cite this article

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Abstract

Membrane proteins on the cell surface perform a myriad of biological functions; however, ligand discovery for membrane proteins is highly challenging, because a natural cellular environment is often necessary to maintain protein structure and function. DNA-encoded chemical libraries (DELs) have emerged as a powerful technology for ligand discovery, but they are mainly limited to purified proteins. Here we report a method that can specifically label membrane proteins with a DNA tag, and thereby enable target-specific DEL selections against endogenous membrane proteins on live cells without overexpression or any other genetic manipulation. We demonstrate the generality and performance of this method by screening a 30.42-million-compound DEL against the folate receptor, carbonic anhydrase 12 and the epidermal growth factor receptor on live cells, and identify and validate a series of novel ligands for these targets. Given the high therapeutic significance of membrane proteins and their intractability to traditional high-throughput screening approaches, this method has the potential to facilitate membrane-protein-based drug discovery by harnessing the power of DEL.

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Fig. 1: The proposed cell-based DEL selection strategy.
Fig. 2: Labelling of FR on HeLa cells.
Fig. 3: BP removal using toehold displacement after the labelling of FR.
Fig. 4: Cell-based labelling of EGFR with antibody-guided probes.
Fig. 5: DEL selection against DNA-tagged FR on HeLa cells.
Fig. 6: Selection of a 30.42-million DEL against FR on live cells.
Fig. 7: Selection of a 30.42-million DEL against DNA-tagged EGFR on live cells.

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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 and Extended Data files. All the published tools and packages used for data analysis are provided with the paper. The Human UniProt database (release-2016_05) used can be accessed at https://www.uniprot.org/proteomes/UP000005640, and the BioNumbers database can be accessed at https://bionumbers.hms.harvard.edu/. Source data are provided with this paper.

Code availability

The custom Python script for sequencing data analysis is freely available for downloading both as part of Supplementary Information and also at GitHub (https://github.com/cenhuang0916/sequencing-data-processing-script.git)

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Acknowledgements

This work was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (AoE/P-705/16, 17321916, 17302817, 17301118, 17111319 and 17303220), Laboratory for Synthetic Chemistry and Chemical Biology of Health@InnoHK of ITC, HKSAR, National Natural Science Foundation of China (21572014, 21877093, 81603067, 21907011 and 91953119), the Fundamental Research Funds for the Central Universities (project numbers 2019CDQYYX018 and 2020CQJQY-Z002), Chongqing Research and Frontier Technology (cstc2020jcyj-jqX0009) and Venture & Innovation Support Program for Chongqing Overseas Returnees (cx2019084) for Y.L. We thank the Centre for PanorOmic Sciences (CPOS) Genomics Core at HKU for NGS support and the CQU-Agilent Joint Lab on DNA-encoded Library for MS support.

Author information

Authors and Affiliations

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

    Yiran Huang, Ling Meng, Yu Zhou, Yi Man Eva Fung, Xiaomeng Li & Xiaoyu Li

  2. Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, China

    Qigui Nie, Shilian Yang, Cen Huang & Yizhou Li

  3. School of Pharmacy, Second Military Medical University, Shanghai, China

    Langdong Chen & Yan Cao

  4. Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, China

    Yizhou Li

  5. Laboratory for Synthetic Chemistry and Chemical Biology of Health@InnoHK, Hong Kong SAR, China

    Xiaoyu Li

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  1. Yiran Huang
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Contributions

Y.H., Y.L. and Xiaoyu Li conceived and designed the experiments. Y.H., L.M., Q.N., Y.Z., L.C., S.Y., Xiaomeng Li and C.H. carried out the experiments and analysed the data. Y.M.E.F. carried out the MS experiments and analysis. Y.C. designed and carried out the SPR experiments and analysis. Y.H., L.M., Q.N., Y.Z., L.C., S.Y., Y.M.E.F., Xiaomeng Li, C.H., Y.C., Y.L. and Xiaoyu Li co-wrote the paper.

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Correspondence to Yan Cao, Yizhou Li or Xiaoyu Li.

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The authors declare no competing interests.

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Peer review information Nature Chemistry thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Labelling of CA-12 on live cells using CBS-guided BP/CP probes.

a) Structures of CBS-BP, NC-BP (a negative binding probe), and CP-1. A549 cells were labelled with CBS-BP/CP-1 and NC-BP (no CBS)/CP-1, respectively, targeting CA-12 on the cell surface. Experimental conditions are the same as in Fig. 2 of the main text. b)-c) Flow cytometry analysis of the labelled A549 cells. d) Column graph summarizing the flow cytometry results. e) CBS-BP and NC-BP were paired with biotin-CP-1, respectively, and used to label CA-12 on A549 and MCF-7 cells. After labelling, the cells were lysed and the biotinylated proteins were analysed with western blotting. M: marker; lanes 1 and 3: with CBS-BP; lanes 2 and 4: with NC-BP. *: endogenous biotinylated proteins. Loading control: internal endogenous biotinylated proteins, marked with *; a portion of each sample was separately blotted for CA-12 and actin as additional input/sample processing controls. In d), n = 3 biologically independent samples were measured; data are presented as mean values ± SD (standard deviation).

Source data

Extended Data Fig. 2 Additional data on toehold displacement of BP after the labelling of FR on HeLa cells.

a) Structures of BP-2 and CP-2 and the labelling scheme. Fluorescent imaging and flow cytometry were used to monitor a series of control experiments. b) Toehold displacement with a mismatched DP after labelling. Unlike the complementary DP shown in Fig. 3, the mismatched DP did not reduce cell fluorescence. c)-e) After toehold displacement with a complementary DP to remove the original BP, a series of control experiments were performed. c): with no FA on BP-3; d): with a mismatched BP-3; e): with free FA competitor (50-fold). Experimental conditions were the same as in Fig. 2 of the main text. f) The same labelling and toehold displacement experiments were performed with the CBS/CA-12 system. Flow cytometry histograms after labelling and before/after DP displacement are shown. Strong fluorescence reduction was observed.

Extended Data Fig. 3 Preparation of antibody–DNA conjugates.

a) The conjugation reaction scheme. b) Native PAGE analysis of the reaction. Lane 1, an anti-EGFR antibody standard; lane 2: the reaction mixture. Marker is based on unmodified antibody. c) The bands a–c in b) were purified and analysed with native PAGE (polyacrylamide gel electrophoresis). d) The unlabelled antibody and purified bands a–c were characterized with ESI-MS; the results confirmed that conjugate b was the mono-DNA–antibody conjugate.

Source data

Extended Data Fig. 4 DEL selection against HeLa cells with different FR expression levels.

a) Besides FA, another FR ligand, methotrexate (MTX) was conjugated with a DNA strand and added to the 4,800-member library as shown in Fig. 5. This 4,802-member DEL was selected against the DNA-tagged FR on HeLa cells with different FR levels; a tag with 7-nt complementarity was used in the selections. b) FP analysis to measure the binding affinity of the MTX–DNA conjugate to the target protein FR, and a Kd of ~26.6 µM was obtained. n = 3 biologically independent FP samples were measured. Data are presented as mean values ± SD (standard deviation) based on biologically independent replicates. c) HeLa cells were cultured in FA-deficient medium and harvested after different passages. Six batches of the cells with different FR expression levels were fluorescently labelled with BP-1/CP-1 and analysed with flow cytometry for each batch. The average number of DNA molecules on each cell was measured; based on a size of 3,000 cubic µm for HeLa cells, the FR concentration for each cell batch was calculated (200 µL selection volume; see Section 9 for calculation method). The 4,802-member DEL was subjected to the same selection procedure as described in Fig. 5,6 against these cell batches, respectively; the selection results were processed also in the same way and summarized in the table. EF: enrichment factor. For each selection, a control without FR tagging was also conducted. d) Column graph summarizing the selection results shown in c).

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Extended Data Fig. 5 DEL selection against HeLa cells labelled with the tags with different lengths of complementary bases.

a) The 4,802-member DEL was selected against the DNA-tagged FR on HeLa cells with different lengths of complementary bases in the tag. Two cell batches (P1 and P4) were used in the selections. The selection procedure and data processing method were the same as in Figs. 5–6. b) The effects of different DNA tag lengths were calculated and summarized in the table; key parameters include: ΔH, ΔS, ΔG, fold of affinity increase, and Kd of the DNA tag/library DNA duplex. c)–d) Column graph and the table summarizing the selection results; EF: enrichment factor. The tag lengths from 6 to 10 bases corresponded to a free energy gain from 5.65 to 9.70 kcal/mol and an affinity increase of ~11,000 to 9-million folds. At 6- and 7-nt, the tag hybridized with library DNA at µM affinity; at 9- and 10-nt, the tag and the library DNA formed stable duplexes, which would increase the affinity of all library compounds to nM binders (Kd: 285 nM and 103 nM, respectively). The results also showed that the enrichment fold of FA and MTX dropped with the 10-nt tag, but many other library compounds were enriched. We reasoned this might be because the 10-nt tag formed stable DNA duplex with all library compounds and resulted in the enrichment of many low-affinity binders. DNA tag shorter than 6-base was not tested because it would have hybridization specificity issue at such a short length; the tag may hybridize with the other regions of the library DNA, instead of the primer-binding site.

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Extended Data Fig. 6 DEL selection against the DNA-tagged CA-12 on live cells.

a) Structures of GLCBS–DNA and CBS–DNA, which were two positive controls added to the 4,800-member DEL for selection against CA-12 on A549 cells. b) Scatter plots of the selection results of the tagged A549 cells (top) and the untagged cells (bottom). The selection experiment condition and data processing protocol are the same as in Fig. 7. x-axis: post-sequencing counts; y-axis: enrichment fold = (post-selection %)/(pre-selection %) of each compound. The positive controls (GLCBS and CBS) are highlighted. c) Calculation of the DNA-tagged CA-12 concentration on A549 cells. The average number of DNA on each cell was determined with flow cytometry.

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Supplementary information

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Supplementary Figs. 1–33, Tables 1–3, discussion, detailed experimental procedures, additional discussion and other additional information.

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Supplementary Table 2

Source data for mass spectrometry analysis.

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Full-length, unprocessed western blots and gels.

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Huang, Y., Meng, L., Nie, Q. et al. Selection of DNA-encoded chemical libraries against endogenous membrane proteins on live cells. Nat. Chem. 13, 77–88 (2021). https://doi.org/10.1038/s41557-020-00605-x

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