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Bottom-up de novo design of functional proteins with complex structural features

Abstract

De novo protein design has enabled the creation of new protein structures. However, the design of functional proteins has proved challenging, in part due to the difficulty of transplanting structurally complex functional sites to available protein structures. Here, we used a bottom-up approach to build de novo proteins tailored to accommodate structurally complex functional motifs. We applied the bottom-up strategy to successfully design five folds for four distinct binding motifs, including a bifunctionalized protein with two motifs. Crystal structures confirmed the atomic-level accuracy of the computational designs. These de novo proteins were functional as components of biosensors to monitor antibody responses and as orthogonal ligands to modulate synthetic signaling receptors in engineered mammalian cells. Our work demonstrates the potential of bottom-up approaches to accommodate complex structural motifs, which will be essential to endow de novo proteins with elaborate biochemical functions, such as molecular recognition or catalysis.

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Fig. 1: Bottom-up design of functional de novo proteins.
Fig. 2: Bottom-up design of six protein folds for the presentation of four different binding motifs.
Fig. 3: Biophysical characterization of lead variants from each topology.
Fig. 4: Crystal structures are in close agreement with the design models.
Fig. 5: Antibody biosensors based on de novo-designed proteins for the detection of epitope-specific responses.
Fig. 6: Bifunctional de novo design controls the activity of synthetic receptors in mammalian cells.

Data availability

Crystal structures have been deposited in the PDB with accession codes 6YWD (4H.01 in complex with Mota Fab) and 6YWC (4E1H.95 in complex with 101F Fab). Amino acid and nucleotide sequences of experimentally characterized variants are available in Supplementary Table 5. Expression plasmids of 4E1H.95_LUMABS, 4E2H.210_LUMABS, 4H.01, 3E2H.37, 4E2H.210, 4E1H.95 and 3H1L_02.395 are available from Addgene under accession numbers 155208, 155209, 155210, 155211, 155212, 155213 and 155198, respectively. Plasmid information for the cellular receptors is available in Supplementary Table 6, and plasmids can be directly requested from the corresponding author.

Code availability

All scripts used for the computational design and for the analysis of next-generation sequencing data have been deposited and are available in the public GitHub repository at https://github.com/LPDI-EPFL/Bottom-up-de-novo-design.

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Acknowledgements

We thank K. Lau, A. Reynaud, L. Durrer, S. Quinche, D. Hacker and F. Pojer in the PTPSP facility at EPFL for protein expression and X-ray crystallography support, D. Demurtas from CIME and S. Nazarov from PTBIOEM for electron microscopy support, L. Menin from the EPFL proteomics core facility for mass spectrometry support, the flow cytometry core facility for technical support and the gene expression core facility for help with next-generation sequencing. We thank V. Olieric at the Paul Scherrer Institute for operation of the X06DA beamline. The computational simulations were facilitated by the CSCS Swiss National Supercomputing Centre as well by SCITAS at EPFL. This work was supported by the Swiss initiative for systems biology (SystemsX.ch), the European Research Council (starting grant no. 716058), the Swiss National Science Foundation (grant no. 310030_163139), the NCCR Molecular Systems Engineering and the NCCR Chemical Biology. F.S. was supported by an SNF/Innosuisse BRIDGE Proof-of-Concept grant, and J.B. was funded by the EPFL Fellows postdoctoral fellowship. T.K. received funding from the Cluster of Excellence RESIST (grant no. EXC 2155) of the German Research foundation and from the German Center of Infection Research, J.T.C. was supported by the ERA-Net PrionImmunity project no. 01GM1503 of the German Federal Ministry of Education and Research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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J.B. and E.A.A. contributed equally to this work. C.Y., F.S. and B.E.C. conceived the work and designed the experiments. C.Y. and F.S. performed computational design and experimental characterization. J.B. developed the TopoBuilder. E.A.A. performed work related to biosensors, and L.S. performed cellular assays. C.Y. solved X-ray structures and L.A.A. performed NMR characterization. J.T.C., X.W., S.R. and S.G. performed experiments and analyzed data. T.J., T.K., M.F. and M.M. contributed to data analysis and interpretation. F.S., C.Y. and B.E.C. wrote the manuscript, with input from all authors.

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Correspondence to Bruno E. Correia.

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Extended data

Extended Data Fig. 1 Structural view of the utilized functional motifs.

Left: Prefusion RSVF structure (PDB 4JHW) with site 0 (purple), site II (cyan) and site IV (orange) highlighted. Center/right: Close-up view on structural motifs, including the 2D10 epitope (wheat) that is presented by the RSV glycoprotein (RSVG), the structure of which has been determined as peptide bound to the 2D10 monoclonal antibody (PDB 5WN9). The secondary structure composition and residue lengths are indicated below. E, beta strand; H, alpha helix and L, loop structure.

Extended Data Fig. 2 Computational design, experimental screening and enrichment analysis of the 3E2H design series.

a, TopoBuilder assembly of the 3E2H topology (left) and a full-atom model after folding and design (right). The logo plots show the sequence diversity in selected core positions as predicted by Rosetta FunFolDes and the diversity for each position encoded in a combinatorial library (using degenerate codons) for yeast display screening. b, Detailed view on all positions experimentally sampled (green). c, Enrichment analysis following next-generation sequencing (NGS) of populations sorted for high affinity binding (x-axis) versus resistance to protease digestion (y-axis). n = 5,377 unique sequences were analyzed that were found under both selection conditions. d, Residue preferences for each position when comparing sequences positively enriched for both binding and protease resistance (c, quadrant I, blue) versus sequences that were negatively enriched (c, quadrant IV, red). 100-200 sequences each were analyzed. The heatmap shows the relative frequency of the respective amino acids in quadrant I versus quadrant IV, showing, for example, an overrepresentation of valine over isoleucine in position 7 in sequences from quadrant I.

Extended Data Fig. 3 Computational design, experimental screening and enrichment analysis of the 4E1H design series.

a, TopoBuilder assembly of the 4E1H topology. b, Detailed view on all positions experimentally sampled (green). c, Enrichment analysis of dual selection pressures, binding to 101F antibody and resistance to protease digestion. n = 92,779 unique sequences were analyzed that were found under both selection conditions. d, Residue preferences for indicated position in positively enriched versus negatively enriched sequences. See Extended Data Fig. 2 caption for further details.

Extended Data Fig. 4 Computational design, experimental screening and enrichment analysis of the 4E2H design series.

a, TopoBuilder assembly of the 4E2H topology and full-atom structure after Rosetta FunFolDes. b, Detailed view on all positions experimentally sampled (green). c, Enrichment analysis. n = 3,903 unique sequences were analyzed that were found under both selection conditions. d, Residue preferences for indicated position in positively enriched versus negatively enriched sequences. See Extended Data Fig. 2 caption for further details.

Extended Data Fig. 5 Computational design, experimental screening and enrichment analysis of the 3H1L_02 design series.

a, TopoBuilder assembly of the 3H1L_02 topology and full-atom structure after Rosetta FunFolDes, the epitope region is highlighted in purple. b, Detailed view on all positions experimentally sampled (green). c, Enrichment analysis. n = 99,338 unique sequences were analyzed that were found under both selection conditions. d, Residue preferences for indicated position in positively enriched versus negatively enriched sequences. See Extended Data Fig. 2 caption for further details.

Extended Data Fig. 6 Computational design, experimental screening and enrichment analysis of the 4H design series.

a, TopoBuilder assembly of the 4H topology and full-atom structure after Rosetta FunFoldDes, the Mota epitope region is colored in cyan and 2D10 epitope is colored in wheat. b, Detailed view on all positions experimentally sampled (green). c, Enrichment analysis. n = 287 unique sequences were analyzed that were found under both selection conditions. d, Residue preferences for indicated position in positively enriched versus negatively enriched sequences. See Extended Data Fig. 2 caption for further details.

Extended Data Fig. 7 Thermal denaturation curve of lead variants from each topology.

Designed proteins are melt from 20 °C to 95 °C as measured by CD. The melting temperature (Tm) is determined by the change of ellipticity at the global curve minimum.

Extended Data Fig. 8 Specificity of LUMABS sensor for the target antibody.

Luminescence spectra of the 4E2H.210 LUMABS sensor in the absence of antibody (black), compared to 2 µM 101 F IgG (blue) or 15 µM cetuximab (CTX, green), an anti-EGFR antibody, showing that the sensor only responds in the presence of epitope-specific antibodies.

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Supplementary Figs. 1–13, Tables 1–7 and refs. 1–3.

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Yang, C., Sesterhenn, F., Bonet, J. et al. Bottom-up de novo design of functional proteins with complex structural features. Nat Chem Biol 17, 492–500 (2021). https://doi.org/10.1038/s41589-020-00699-x

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