Abstract
Protein lipidation, which regulates numerous biological pathways and plays crucial roles in the pharmaceutical industry, is not encoded by the genetic code but synthesized post-translationally. In the present study, we report a computational approach for designing lipidation mimics that fully recapitulate the biochemical properties of natural lipidation in membrane association and albumin binding. Furthermore, we establish an engineered system for co-translational incorporation of these lipidation mimics into virtually any desired position of proteins in Escherichia coli and mammalian cells. We demonstrate the utility of these length-tunable lipidation mimics in diverse applications, including improving the half-life and activity of therapeutic proteins in living mice, anchoring functional proteins to membrane by substituting natural lipidation, functionally characterizing proteins carrying different lengths of lipidation and determining the plasma membrane-binding capacity of a given compound. Our strategy enables gain-of-function studies of lipidation in hundreds of proteins and facilitates the creation of superior therapeutic candidates.
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Data availability
Supplementary information (methods and figures) and chemical compound information are available in the online version of the paper. Correspondence and requests for materials should be addressed to S.L. Source data are provided with this paper.
Code availability
The source codes for python used for clog(P) and ΔG plotting are available in Supplementary Information.
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Acknowledgements
We thank the National Key R&D Program of China (grant no. 2019YFA09006600), the National Natural Science Foundation of China (grant nos. 22222705, 92253302, 91953113 and 21877096), Young Scientists Fund of the National Natural Science Foundation of China (grant no. 22207095 for W.D.), China Postdoctoral Science Foundation (grant no. 2019M652072 to W.D.) and the Fundamental Research Funds for the Central Universities (grant no. K20220228) for financial support. We are grateful to the core facility of the Life Sciences Institute and X. He for helpful discussions.
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Authors and Affiliations
Contributions
S.L. conceived the idea and supervised the study. W.D. and C.L. conducted most experiments and analyzed the data together. Y.C. and C.F. helped with the virtual screening, J.G. and L.H. assisted with mice experiments. L.Z., Y.Y. and X.-H.F. provided technical advice. S.L. and W.D. wrote the manuscript. All authors commented on the final draft of the manuscript.
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The authors declare the following competing interests: S.L., W.D., C.L. and Y.C. are listed as inventors on a patent application describing the development and application of genetically encoded lipidation mimics (Chinese patent application no. 202310095754.9). All remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 The computationally assisted design and screening of lipidation mimics.
(a) The procedure for predicting the HSA binding affinity (ΔG) of lipidation mimics. Lipidation mimics were docked to 7 sites of HSA (highlighted with red dash boxes) by AutoDock Vina. (b) The procedure for calculating RMSD. RMSD, defined as the average distance between the coordinates of the lipidation mimics obtained by docking and the coordinates of the reference ligand generated from the crystal structure, was calculated by LigRMSD. (c) The chemical structures of selected lipidation mimics and myristate whose linear carbon chains were numbered and colored in red. (d) The predicted cLog(P) of lipidation mimics in (C). (e) The HSA binding affinity of selected lipid mimics and fatty acids in the 7 binding sites of HSA. (f) The docking structure of 4HexyF (white) in the 7 binding sites of HSA. The amino acids surrounding 4HexyF for π-π interactions were highlighted by magenta. (g) Surface plasmon resonance analysis of HSA binding affinity of selected lipidation mimics. Data were fitted to single affinity mode and the binding affinities were shown.
Extended Data Fig. 2 The detailed information of Fig. 1b.
Every symbol was numbered as Supplementary Data 1. HepoK, 4HexyF, 4OctyF, 4NonyF and 4DecyF were numbered as K2, F1, F2, F3 and F4 respectively.
Extended Data Fig. 3 Computationally aided design of functional lipidation mimics.
(a) Candidate lipid mimics with -ΔG > 8.2 and cLogP > 4.0 were plotted based on their ΔG and cLog(P) values. The dash line indicated the RMSD threshold of around 2.20 for Fs, 2.29 for Ws, and 2.93 for As and Ks. UAAs with RMSD above the threshold were colored with grey. UAAs with RMSD values below the threshold on the left side of the dash line had a high chance of being recognized by orthogonal aaRSs. (b) The chemical structure of candidate lipid mimics. (c) Phenylalanine derivatives in (B) were plotted based on RMSD and aliphatic carbon chains length. The RMSD value was dramatically increased when length of aliphatic carbon chains was greater than 9.
Extended Data Fig. 4 The chemical synthesis and genetic encoding of lipidation mimics.
(a) The chemical synthesis of 4HexyF, 4OctyF, 4NonyF and 4DecyF. A three-step synthetic route was developed with the isolation yield of each synthetic step shown under the products. (b) The model structure of human mitochondrial PheRS in complex with 4HexyF. The gatekeeper residues colored in magenta displayed steric hindrance with 4HexyF. (c) The model structure of LipRS-1 in complex with 4HexyF (cyan). Residues around 4HexyF were shown as sticks with three key mutations highlighted in magenta and the others in blue. (d) The amber suppression assay of lipidation mimics by the indicated LipRS-2 variants (n = 2 biologically independent experiments). (e) The gate strategy of FACS used in Fig. 4a.
Extended Data Fig. 5 The high HSA binding affinity of proteins incorporated with lipidation mimics.
(a) Coomassie blue staining gel analysis of purified GLP-1 variants incorporated with indicated lipidation mimics. The experiment in the figure was repeated twice with similar results. (b) Mass spectrometry characterization of the fidelity of lipid mimics on GLP-1. The expected MW (-Met) of 4HexyF and 4OctyF incorporation were 15463 and 15491 Da, and the observed MW (-Met) were 15461 and 15490 Da, respectively. (c) MST analysis of affinity of GLP-1 variants for HSA. The data were fitted with Kd model by MO.Affinity analysis software and the binding affinity were shown. Error bars represented ± standard error of the mean (n = 3 biologically independent experiments). (d, e) ITC analysis of the affinity of GFP variants (D) or Neo-2/15 variants (E) for HSA. The data were fit to a single binding site model, and fitting thermostability parameters were shown in the figure.
Extended Data Fig. 6 Evaluating the therapeutic effect of lipidation mimics and Neo-2/15.
CT26 tumor growth (a) and Kaplan-Meier survival curves (b) in mice after treatment with 4OctyF, Neo-2/15-F and 4OctyF plus Neo-2/15-F, respectively, were quantified. Statistical significance of tumor growth was quantified with unpaired two-tailed t-test (n = 4). Mice survival was analyzed with a log-rank test (n = 4 biologically independent experiments). Error bars represents ± standard error of the mean (n = 4 biologically independent experiments).
Extended Data Fig. 7 Genetically encoded lipidation mimic for efficient membrane association.
(a) The representative images (n = 3) of G2F mutation of indicated proteins showed cytoplasmic localization. (b, d) The representative images (n = 3) of XRP2 variants (B) and Lck variants (D). (c, e) Fluorescence intensity profiles across the yellow line in (B, D) indicated the P/C ratio of XRP2 variants (C) and Lck variants (D), respectively. (f) The representative images (n = 3) of XRP2 and Lck variants incorporated with 4HexyF. The Scale bar: 10 μm.
Extended Data Fig. 8 The 4OctyF incorporation mimic the function of Gα subunit.
(a) the assembly model of G-protein αβγ heterotrimers. (b) Representative images (n = 3) showed that 4OctyF incorporation of Gα subunit could bind and recruit Gβγ complex to plasma membrane. Scale bar: 10 μm.
Extended Data Fig. 9 The membrane association capability of length-tunable lipidation mimics.
(a) The representative images (n = 3) of protein with the construct cartoon shown was incorporated with length-tunable lipidation mimics. Scale bar: 10 μm. (b) Fluorescence intensity profiles across the yellow lines in (A) showed perinuclear area accumulation of protein in the 4OctyF group. (c) Mass spectrometry characterization of the fidelity of lipidation mimics on polyK-EGFP. The expected MW (-Met) of 4HexyF and 4OctyF incorporation were 29648 and 29676 Da, and the observed MW (-Met) were 29649 and 29677 Da, respectively. (d) The cartoon procedure of in vitro binding affinity with liposomes. The liposome binding affinity of protein was determined by measuring the fraction of liposome-protein complex with liposomes co-sedimentation assay. (e) The cLogP threshold (x) for efficient membrane anchoring was between 5.07 (laurate) and 5.3 (4OctyF) for the peptide substrates with similar sequence. The peptide modified by a compound with cLogP value larger than x (green hexagon) tended to enrich at plasma membrane, while compound with cLogP value less than x (blue square) distributed diffusely in cytoplasm.
Supplementary information
Supplementary Information
Supplementary Notes 1 and 2, and Figs. 1–18.
Supplementary Data 1
The chemical structure and biophysical properties of lipidation mimics that we designed.
Source data
Source Data Fig. 1
Source data of clog(P) and ΔG of lipidation mimics in Fig. 1.
Source Data Fig. 2
Raw plate reader data and raw MS data in Fig. 2.
Source Data Fig. 3
Raw MS data, ITC data and tumor data in Fig. 3
Source Data Fig. 4
Raw FACS statistical data.
Source Data Fig. 4
Unprocessed western blots in Fig. 4.
Source Data Fig. 5
Raw micrograph profiling data and plate reader data in Fig. 5.
Source Data Extended Data Fig. 1
Raw SPR data in Extended Data Fig. 1.
Source Data Extended Data Fig. 3
Raw data for RMSD plotting in Extended Data Fig. 3.
Source Data Extended Data Fig. 4
Raw plate reader data in Extended Data Fig. 4.
Source Data Extended Data Fig. 5
Raw MS data and MST data in Extended Data Fig. 5.
Source Data Extended Data Fig. 5
Unprocessed SDS–PAGE gel in Extended Data Fig. 5.
Source Data Extended Data Fig. 6
Raw data for tumor growth in Extended Data Fig. 6.
Source Data Extended Data Fig. 7
Raw micrograph profiling data in Extended Data Fig. 7.
Source Data Extended Data Fig. 9
Raw micrograph profiling data and MS data in Extended Data Fig. 9.
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Ding, W., Liu, C., Chen, Y. et al. Computational design and genetic incorporation of lipidation mimics in living cells. Nat Chem Biol 20, 42–51 (2024). https://doi.org/10.1038/s41589-023-01400-8
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DOI: https://doi.org/10.1038/s41589-023-01400-8
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