Abstract
De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37–43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.
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Acknowledgements
We thank M. Levitt and M. Zhang for discussions, A. Ford for data analysis advice, and Rosetta@Home participants for donating computing time. D.-A.S. thanks T. J. Brunette, J. E. Hsu and M. J. Countryman for their assistance. R.J. thanks K. Perry for X-ray data collection. We acknowledge funding support from: Life Sciences Discovery Fund Launch grant 9598385 (A.C.); PEW Latin-American fellow in the biomedical sciences and a CONACyT postdoctoral fellowship (D.-A.S.); Merck fellow of the Life Sciences Research Foundation (G.J.R.); CONACyT and Doctorado en Ciencias Bioquímicas UNAM (R.V.); NIH (R56AI117675) and Molecular Basis of Viral Pathogenesis Training Grant (T32AI007354-26A1) (S.M.B.); Investigator in the Pathogenesis of Infectious Disease award from the Burroughs Wellcome Fund and NIH (1R01NS080833) (M.D.); CoMotion Mary Gates Innovation Fellow program (T.C.); generous gift from Rocky and Genie Higgins (C.B.); Shenzhen Science and Technology Innovation Committee (JCYJ20170413173837121), Hong Kong Research Grant Council C6009-15G and AoE/P-705/16 (X.H.); PAPIIT UNAM (IN220516), CONACyT (254514) and Facultad de Medicina UNAM (D.A.F.-V.); NIAID grants (AI091823, AI123920, and AI125704) (R.J.); NIAID grant 1R41AI122431 (M.T.K. and D.H.F.); NIAID grant 1R21AI119258 and Life Sciences Discovery Fund grant 20040757 (D.H.F.). We acknowledge computing resources provided by the Supercomputing Laboratory at King Abdullah University of Science and Technology and the Hyak supercomputer system funded by the STF at the University of Washington. The Berkeley Center for Structural Biology is supported in part by the NIH, NIGMS, and HHMI. The Advanced Light Source is a DOE Office of Science User Facility under contract no. DE-AC02-05CH11231. The Northeastern Collaborative Access Team beamlines are funded by NIGMS grant P41 GM103403 and a NIH-ORIP HEI grant (S10OD021527). Advanced Photon Source is a U.S. DOE Office of Science User Facility operated by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
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A.C., D.-A.S., G.J.R., D.H.F. and D.B. designed the research; A.C., D.-A.S., and G.J.R. contributed equally; D.R.H., R.V., and P.M. contributed equally; A.C., D.-A.S., G.J.R., D.R.H., R.V., and C.D.B. designed proteins. A.C., D.-A.S., D.R.H., R.V., performed binding experiments; S.M.B. solved influenza co-crystal structures; P.M., M.T.K., A.B., C.M.J. and J.T.F. performed influenza experiments; L.Z. performed molecular dynamics simulations; K.-H.L. and G.Y. solved BoNT co-crystal structures; S.-I.M. performed BoNT neutralization assays; I.G. and C.M.B. prepared yeast and next generation sequencing; T.C. performed protease-resistance characterization; L.C. performed protein purification. All authors analysed data. D.A.F.-V., L.S., M.D., X.H., R.J., I.A.W., D.H.F. and D.B. supervised research. A.C., D.-A.S., G.J.R., D.R.H., D.H.F. and D.B. wrote the manuscript.
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Authors declare competing interests: A.C., M.T.K., D.H.F. and D.B. are co-founders and stockholders of Virvio, Inc., a company that aims to develop the therapeutics described in this manuscript. A.C., D.-A.S., G.J.R., C.D.B. and D.B. are co-inventors on a U.S. provisional patent application (No. 62/471,637) that incorporates discoveries described in this manuscript.
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Extended data figures and tables
Extended Data Figure 1 Target proteins architecture and interactions with anti-BoNT/B and anti-influenza motifs.
a, Full complex of BoNT, showing heavy chain binding domain (HCB) target epitope position in relation to catalytic and translocation domains. Inset shows inhibitory fragment Syt-II (in orange) bound to HCB with hotspots shown as sticks, and grey areas excluded from design calculations. b, Crystal structure of SC1918/H1 showing HA1 and HA2 subunits in complex with HB36.3. Inset shows detailed view of HB36.3 (in green) bound to stem region epitope with hotspots shown as sticks, and grey areas excluded from design calculations. c, Crystal structure of SC1918/H1 showing HA1 and HA2 subunits in complex with HB80.4. Inset shows detailed view of HB80.4 (in magenta) bound to stem region epitope with hotspots shown as sticks, and grey areas excluded from design calculations.
Extended Data Figure 2 Categorization of binders from high-throughput sequencing data of yeast-display FACS-sorted yeast pools.
a, Schematic representation of a resulting yeast pool experiment transformed with four genes, corresponding to four different binder designs (colours: blue, orange, grey, yellow). The first column represents the initial yeast pool, which presents some variability in the initial number of cells transformed with each gene. Subsequently, the cells are subject to different stringencies of selection condition (display, high, medium and low target concentrations). The number of cells selected during FACS (see Methods) is proportional to both the binding affinity and the fractional population of the design. b, Instead of observing a ‘classical’ readout where each measurement is directly proportional to the amount of binding, the result is a convoluted readout (using high-throughput sequencing of each FACS of selected yeast pools under different conditions, see Methods) of both the population fraction and the binding strength. c, Our method of analysing the strength of an individual design is to assign each of them to a binding condition (category) if they produce a peak in its enrichment (as compared to its own initial population in the unselected, but displaying, population). Since, at higher categories, ‘better’ binders will always out-compete weaker ones, this method clusters binders into categories of binding (for example, weak, medium, or strong). If protease is used to further select the populations for stability, the same concept applies (see Fig. 2).
Extended Data Figure 3 Molecular dynamics simulations to assess the flexibility of mini-protein binder designs, their binding motifs and hotspots.
a, Schematic representations of the helical segments and hotspots used to calculate the average r.m.s.d. for mini-protein binders containing binding motifs from HB36, HB80 and Syt-II. The four conserved hotspots (orange) used to calculate the average r.m.s.d. of each binding motif are also shown. b, Top, average r.m.s.d.s (with respect to the designed bound conformation) of the whole mini-proteins versus those of the hotspots. The results for non-binders and binders are shown in black and red, respectively. Bottom panel, same as top, except that the x-axis displays the r.m.s.d.s of the entire helical motif. These results were obtained from an aggregation of 108 μs molecular dynamics simulations, from a representative sample of designs (143 for BoNT and 146 for influenza, see Methods for details). The r.m.s.d. values for hotspot residues were calculated using a subset of side-chain heavy atoms that are invariant to the rotation of the aromatic ring (CG and CZ for Phe and Tyr). The backbone heavy atoms were used for the r.m.s.d. calculations of ‘binding helical motif’ and ‘whole protein’. c, The convergence of molecular dynamics simulations discriminates binders and non-binders as a function of simulation length (30 ns, 40 ns, 50 ns and 100 ns), subject to a similar amount of total sampling. The results show that simulations of 50 ns in duration are sufficient to discriminate the stability of binders and non-binders, even though longer molecular dynamics simulations (such as 100 ns) may further improve the discrimination power. Ten randomly selected mini-proteins designed against BoNT (which are also included in b) were used in this figure. d, Similar to Fig. 3d, the normalized traces of the histograms (fitted using a normal probability density function) show that, for both targets, the designs that are binders (cyan, yellow and red lines) show trends with smaller fluctuations in hotspot residues than non-binders (blue lines); however, no particular trend is observed regarding strength of binding.
Extended Data Figure 4 Circular dichroism studies.
a, Designed mini-proteins that were co-crystallized in complex with their respective targets (as shown in Fig. 4). Designed anti-HA mini-protein HB1.6928.2.3 does not denature up to a temperature of 95 °C. Designed anti-BoNT/B mini-protein shows partial denaturation at 95 °C that is completely reversible after fast-cooling to 25 °C. Black shows the circular dichroism spectrum at 25 °C, red at 95 °C, and yellow at 25 °C (after fast refolding, 5 min). Proteins were measured at 0.25 mg ml−1 in PBS buffer pH 7 (see Methods). b, Proteins that were solubly expressed or chemically synthesized. Plots are analogous to a. HB1.10027.3 contains two disulfides, HB1.6394.2.3 contains three disulfides, Bot.6782.4, Bot.6827.4, Bot.7075.4, Bot.4024.4, Bot.3318.4, Bot.5721.4, and Bot.5916.4, each contain one disulfide bond. The rest of the proteins were designed without disulfide bonds. c, Three disulfide-containing proteins with and without reducing agent. Plots are analogous to a. Proteins were measured at 0.25 mg ml−1 in PBS buffer pH 7 without (top row) and with (bottom row) the reducing agent TCEP. The disulfides are shown to be crucial for the thermal stability of these disulfide-containing proteins (HB1.6928.2.3 contains two disulfides, Bot.2110.4 and Bot.3194.4 each contain one disulfide).
Extended Data Figure 5 Trypsin resistance of HA binders.
Chemically synthesized HA binder (0.3 mg ml−1) was incubated in PBS with various dilutions of trypsin (52 μM stock) for 20 min at room temperature. Reactions were quenched with addition of 1% weight per volume BSA and samples run on SDS–PAGE gel. The relative concentrations of trypsin are shown at the top. ImageJ was used to quantify the intensity of each band (below the band). a, Both HB36.6 and HB1.5702.3 show weaker gel bands at trypsin concentrations higher than 0.055 stock (2.86 μM), indicating proteolytic degradation. HB1.6928.2 and HB1.6394.2, both of which contain disulfides, show no degradation at any trypsin concentration. b, Scatter plot of gel intensities in a.
Extended Data Figure 6 Omit map of HB1.6928.2.3.
a, A simulated annealing FO−FC omit map for HB1.6928.2.3 (green) residues 10–22 (contoured at 3σ) shows clear density for amino-acid side chains at the interface (dark blue HA1, light blue HA2). A single residue (Asn32), in a loop between the first and second β–strands, is not observed in the electron density. b, 2FO−FC map for Bot.671.2 (green) residues 2-13 (contoured at 2σ) shows clear density for side chains at the interface except for the flexible lysine residue. BoNT HCB is shown in dark blue. The entire backbone, interface, and core residues for Bot.671.2 are all well resolved in the electron density map.
Extended Data Figure 7 In vitro neutralization of BoNT/B.
a, b, Immunoblots of cultured primary rat cortical neurons that were exposed to BoNT/B (20 nM) or BoNT/A (10 nM) with or without GST–Syt-II or Bot.671.2 (see Methods). The supernatants of lysed neurons were collected for immunoblot analysis to detect the indicated proteins, and actin served as control for loading. The designed mini-protein appears to confer protection against degradation of VAMP2, but not against degradation of the negative control, SNAP25 (the intracellular target of BoNT/A). c, Immunocytochemistry for detection of BoNT/B in neurons (see Methods). Left, negative control (no toxin); middle, positive control (cells incubated with 20 nM of BoNT/B for 10 min); right, near-total protective effect against 20 nM of BoNT/B conferred by co-incubating the cells with 600 nM of the design Bot.671.2. Top panels show a representative image of fluorescence microscopy for the detection of BoNT/B; bottom panels show backfield illumination microscopy for the same area.
Extended Data Figure 8 In vitro neutralization of influenza.
Comparison of in vitro neutralization of influenza viruses by HB36.6, FI6v3 and the designed mini-protein HB1.6928.2.3. Each antiviral was compared for its efficiency (EC50) in inhibiting the infection of Madin–Darby canine kidney cells by a range of influenza strains. It is clear that HB1.6928.2.3 most efficiently inhibited infection for all of the group-1 influenza strains tested (H1N1, H5N1 and H6N2). As expected, no neutralization was observed against H3N2 (group 2). In all experiments, n = 3 independent samples were tested for each condition, except for T/Mass/1965 (H6N2) and HK/ X31 (H3N2), for which n = 2 samples were tested. Dots show raw values for independent tests and whiskers show ± 1 s.d.
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Chevalier, A., Silva, DA., Rocklin, G. et al. Massively parallel de novo protein design for targeted therapeutics. Nature 550, 74–79 (2017). https://doi.org/10.1038/nature23912
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DOI: https://doi.org/10.1038/nature23912
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