IMMUNOGEN
20220249649 · 2022-08-11
Inventors
- Bruno Correia (Lausanne, CH)
- Fabian Sesterhenn (Lausanne, CH)
- Che Yang (Lausanne, CH)
- Jaume Bonet (Lausanne, CH)
Cpc classification
C12N7/00
CHEMISTRY; METALLURGY
A61K39/00
HUMAN NECESSITIES
International classification
Abstract
Polypeptides useful in the preparation of vaccine compositions against RSV are provided. Also disclosed are methods of enhancing subdominant antibody responses in a subject.
Claims
1. A vaccine composition against a target pathogen, the composition comprising a plurality of non-naturally occurring immunogenic polypeptides; at least a first of said immunogenic polypeptides comprising a mimic peptide having an amino acid sequence having a tertiary structure which, when folded, mimics a complex and/or discontinuous neutralisation epitope from said target pathogen.
2. The vaccine composition of claim 1, wherein each of said plurality of non-naturally occurring immunogenic polypeptides comprises a mimic peptide having an amino acid sequence which, when folded, mimics a complex and/or discontinuous neutralisation epitope from said target pathogen.
3. The vaccine composition of claim 2, wherein each of said complex and/or discontinuous neutralisation epitopes are non-overlapping.
4. The vaccine composition of any preceding claim, wherein said target pathogen is RSV.
5. The vaccine composition of claim 4, wherein said complex and/or discontinuous neutralisation epitopes are selected from the group consisting of RSV site 0, site II, and site IV.
6. The vaccine composition of claim 5, wherein said immunogenic peptides are selected from the peptides described in tables 3 to 6, and preferably from tables 5 or 6.
7. The vaccine composition of any preceding claim wherein said immunogenic peptide comprises a scaffold, preferably a peptide scaffold, which presents the mimic peptide so as to assist the mimicking of the complex and/or discontinuous neutralisation epitope.
8. The vaccine composition of claim 7 wherein said scaffold is selected from RSVN and ferritin.
9. The vaccine composition of any preceding claim, in combination with a vaccine composition comprising a native immunogen from the target pathogen.
10. A vaccine composition comprising the S0_2.126 peptide sequence as described herein, and the S4_2.45 peptide sequence as described herein, and optionally further comprising the FFL_001 or FFLM peptides.
11. A vaccine composition of any preceding claim, wherein said target pathogen is RSV, for use in a method for immunising a subject against RSV, the method comprising a) administering said vaccine composition to a subject; and b) prior to said administration, administering a further vaccine composition comprising an RSV-derived protein or glycoprotein, preferably the RSVF glycoprotein, or wherein the vaccine composition of any preceding claim is administered to a subject who has previously been exposed to RSV infection.
12. A method for designing a peptide to mimic a complex and/or discontinuous structural configuration of a target peptide, the method comprising the steps of: determining a complex and/or discontinuous structural configuration of a target peptide to mimic; identifying a preliminary mimic peptide having an amino acid sequence; determining likely structural configuration of said preliminary mimic peptide amino acid sequence by in silico analysis of said sequence; performing directed evolution on said preliminary mimic peptide to generate a range of variants of said peptide; (preferably wherein directed evolution may be performed by mutagenesis to generate variants and expression of said variants); and selecting for variants of said peptide which display an improvement in a desired characteristic seen in said target peptide (said characteristic may be, for example, binding affinity to a target such as an antibody; thermal stability; susceptibility or resistance to an enzyme).
13. The method of claim 12 further comprising the steps of identifying a plurality of said variants having improvements, and providing a further peptide having a combination of variations from said plurality of variants.
14. The method of claim 12 or 13 wherein said step of identifying a preliminary mimic peptide comprises selecting a peptide from a peptide database having a structural similarity to the desired target peptide; or wherein said step comprises combining an amino acid sequence from said target peptide with one or more structural peptide elements such that said preliminary mimic peptide sequence has a structural similarity to the desired target peptide.
Description
BRIEF SUMMARY OF THE FIGURES
[0034] These and other aspects of the invention will now be described in detail, and with reference to the following figures.
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DETAILED DESCRIPTION OF THE INVENTION
De Novo Design of Immunogens with Structurally Complex Epitopes
[0067] Designing proteins with structurally complex functional sites has remained a largely unmet challenge on the field of computational protein design. We sought to design accurate mimetics of RSV neutralization epitopes, which have been particularly well studied structurally, and evaluate their functionality in immunization studies. We chose antigenic sites 0 and IV (
[0068] The computational design of proteins mimicking structural motifs has previously been performed by first identifying compatible protein scaffolds, either from naturally occurring structures or built de novo, which then serve as design templates to graft the motif (5, 6, 8, 15, 16). Given the structural complexity of sites 0 and IV, this approach did not yield any promising matches even with loose structural criteria (
[0069] Thus, for site IV, we noticed that a small structural domain that resembles an immunoglobulin fold containing the epitope could be excised from the preRSVF structure, hypothesizing this would be a conservative approach to maintain its native, distorted epitope structure (
[0070] The discontinuous structure of site 0 was not amenable for a domain excision and stabilization approach. We searched for template structures that mimicked the helical segment of the epitope, and simultaneously allowed to graft the loop segment, and selected a designed helical repeat protein as design template (PDB 5cwj) (
[0071] The primary goals for the designs were achieved in terms of the stabilization of irregular and complex binding motifs in a conformation relevant for antibody binding, however, the overall strategy presented important limitations with respect to its general utility. Despite the large number of structures available to serve as design templates, the number of those that are practically useful for the design of functional proteins becomes increasingly limited with the structural complexity of the motif. As described above, suboptimal design templates require extensive backbone flexibility on the design process and multiple rounds of directed evolution until a sequence with high-affinity binding is identified. Additionally, the starting topology determines the overall shape of the designed protein, which may be suboptimal for the accurate stabilization of the motif, and may oppose unwanted tertiary steric constraints that interfere with the designed function. In particular, for immunogen design it would be advantageous to preserve native-like accessibility of the epitope to maximize the induction of functional antibodies that can cross-react with the proteins presented by the pathogen. An illustrative example on how a template-based design approach can fail to fulfil these criteria is the comparison between the quaternary environment of the site 0 epitope in preRSVF and S0_1.39 showing that this topology does not mimic such environment, albeit allowing the binding of several monoclonal antibodies (
[0072] To overcome these limitations, we developed a template-free design protocol—the TopoBuilder—that generates tailor-made topologies to stabilize complex functional motifs. Within the TopoBuilder, we sample parametrically the placement of idealized secondary structure elements which are then connected by loop segments, to assemble topologies that can stabilize the desired conformation of the structural motif. These topologies are then diversified to enhance structural and sequence diversity with a folding and design stage using Rosetta FunFoldDes (see
[0073] To present antigenic site IV, we designed a fold composed of a β-sheet with 4 antiparallel strands and one helix (
[0074] We screened a defined set of computationally designed sequences using yeast display and applied two selective pressures—binding to 101F and resistance to the unspecific protease chymotrypsin, an effective method to digest partially unfolded proteins (5, 20, 21). Deep sequencing of populations sorted under different conditions revealed that S4_2_bb2-based designs were strongly enriched under stringent selection conditions for folding and 101F binding, showing that subtle topological differences in the design template can have substantial impact on function and stability. We expressed 15 S4_2_bb2 design variants and successfully purified and biochemically characterized 14. The designs showed mixed alpha/beta CD spectra and bound to 101F with affinities ranging from 1 nM to 1 μM (
[0075] Similarly, we built a minimal de novo topology to present the tertiary structure of the site 0 epitope. The choice for this topology was motivated by the fact that site 0, in its native environment preRSVF, is accessible for antibody binding from diverse angles (14), in contrast to the S0_39 natural template which topologically constrained site 0 accessibility (
[0076] We explored the topological space within the shape constraints of preRSVF and built three different helical orientations that support both epitope segments. Evaluation of the designed sequences with Rosetta abinitio showed that only sequences generated based on one of the three topologies (S0_2_bb3) presented a funnel-shaped energy landscape (
[0077] We selected 5 sequences, differing in 3-21 positions, for further biochemical characterization (
[0078] Overall, the properties of the designs generated by topological assembly with the TopoBuilder showed improved binding affinities and thermal stabilities as compared to those using available structural templates. To investigate whether this design and screening procedure yielded scaffolds that better mimicked the viral epitope presented, or rather revealed sequences with a highly optimized interface towards the antibodies used during the selection, we determined the affinity of S4_2.45 and S0_2.126 against a panel of site-specific antibodies. Compared to the first-generation designs, S4_2.45 and S0_2.126 showed large affinity improvements to diverse panels of site-specific antibodies, exhibiting a geometric mean affinity closely resembling that of the antibodies to preRSVF (
De Novo Designed Topologies Adopt the Predicted Structures
[0079] To evaluate the structural accuracy of the computational design approach, we solved the crystal structure of S4_2.45 in complex with 101F at 2.6 Å resolution. The structure closely matched our design model, with a full-atom RMSD of 1.5 Å. The epitope was mimicked with an RMSD of 0.135 ↑, and retained all essential interactions with 101F (
[0080] Next, we solved an unbound structure of S0_2.126 by NMR, confirming the accuracy of the designed fold with a backbone RMSD between the average structure and the model of 2.8 Å (
Cocktails of Designed Immunogens Elicit Neutralizing Antibodies In Vivo
[0081] Lastly, we sought to evaluate the designed antigens for their ability to elicit antibody responses in vivo. Our rationale for combining site 0, II and IV immunogens in a cocktail formulation is that all three sites are non-overlapping, as verified by electron microscopy analysis (
[0082] In mice, Trivax1 elicited low levels of RSVF cross-reactive antibodies, and sera did not show
[0083] RSV neutralizing activity in most animals (
[0084] In parallel, we sought to test the potential of a trivalent immunogen cocktail in NHPs. The previously designed site II immunogen showed promise in NHPs, but induced neutralizing titers were low and inconsistent across animals, requiring up to five immunizations to elicit neutralizing antibodies in 2/4 animals (11). We immunized seven RSV naïve NHPs with Trivax1, as detailed in
[0085] While immunization studies in naïve animals are important to test the designed immunogens, an overarching challenge for vaccine development to target pathogens such as RSV, influenza, dengue and others is to focus or reshape pre-existing immunity of broad specificity on defined neutralizing epitopes that may be of higher-quality and mediate long-term protection (23). To mimic a serum response of broad specificity, we immunized 13 NHPs with prefusion RSVF. All animals developed strong preRSVF-specific titers and cross-reactivity with all the epitope-focused immunogens, indicating that epitope-specific antibodies were primed and recognized by the designed immunogens (
[0086] Altogether, we concluded that both design strategies yielded antigens for complex neutralization epitopes that induce neutralizing antibodies upon cocktail formulation, providing a strong rationale for including multiple, ideally non-overlapping epitopes in an epitope-focused vaccination strategy. While the first-generation immunogens were inferior according to biophysical parameters and failed to induce neutralization in mice, but were successful under two different immunological scenarios in NHPs, we show that a second generation with improved biophysical properties and proven accurate mimicry of the epitope can now induce neutralizing antibodies in mice. This is an important step as it now allows to optimize and test the different nanoparticles, formulations and delivery routes in a small animal model, and we foresee that these second-generation immunogens will prove superior in inducing neutralizing serum responses in NHPs.
Discussion & Conclusions
[0087] Here, we have showcased computational protein design strategies to design accurate mimetics of structurally complex epitopes, and validated their functionality to elicit neutralizing antibody responses in cocktail formulations both in mice and NHPs.
[0088] We have shown that through computational design of pre-existing templates with full backbone flexibility, irregular and discontinuous epitopes were successfully stabilized in heterologous scaffolds. However, this design strategy required extensive in vitro evolution optimization and the resulting scaffolds remained suboptimal regarding their biochemical and biophysical properties. In addition, the lack of precise topological control of the designed proteins is a major limitation for the design of functional proteins that require specific topological similarity on top of the local mimicry of the transplanted site. For instance, the design template of the site 0 immunogen did not mimic the quaternary environment of the epitope of interest, which may have contributed to the low levels of functional antibodies induced in mice. To overcome these limitations, we developed the TopoBuilder, a motif-centric design approach that tailors a protein fold directly to the functional site of interest. Compared to previously employed de novo design approaches, in which a stable scaffold topology was constructed first and endowed with binding motifs in a second step (5), our method has significant advantages for structurally complex motifs. First, it allows to tailor the topology to the structural requirements of the functional motif from the beginning of the design process, rather than through the adaptation (and often destabilization) of a stable protein to accommodate the functional site. Second, the topological assembly and fine-tuning allowed to select for optimal backbone orientations and sequences that stably folded and bound with high affinity in a single screening round, without requiring further optimization through directed evolution, as often used in computational protein design efforts (5, 24, 25). Together, our approach enabled the computational design of de novo proteins presenting irregular and discontinuous structural motifs that are typically required to endow proteins with diverse biochemical functions (e.g. binding or catalysis), thus providing a new means for the de novo design of functional proteins.
[0089] On the functional aspect of our design work, we showed in vivo that these immunogens consistently elicited neutralizing serum levels in mice and NHPs as cocktail formulations. The elicitation of focused neutralizing antibody responses by vaccination remains the central goal for vaccines against pathogens that have frustrated conventional vaccine development efforts. Using RSV as a model system, we have shown that cocktails of computationally designed antigens can robustly elicit neutralizing serum levels in naïve animals. These neutralization levels were much superior to any previous report on epitope-focused immunogens (11) and provide a strong rationale for an epitope-focused vaccination strategy involving multiple, non-overlapping epitopes. Also, their capability to dramatically reshape the nature of non-naïve repertoires in NHPs, addresses an important challenge for many next-generation vaccines to target pathogens for which efficacious vaccines are needed. An important pathogen from this category is influenza, where the challenge is to overcome established immunodominance hierarchies (26) that favour strain-specific antibody specificities, rather than cross-protecting nAbs found in the hemagglutinin stem region (27). The ability to selectively boost subdominant nAbs targeting defined, broadly protective epitopes that are surrounded by strain-specific epitopes could overcome a long-standing challenge for vaccine development, given that cross-neutralizing antibodies were shown to persist for years once elicited (28). A tantalizing future application for epitope-focused immunogens could marry this technology with engineered components of the immune system and they could be used to stimulate antibody production of adoptively transferred, engineered B-cells that express monoclonal therapeutic antibodies in vivo (29).
[0090] Altogether, this study provides a blueprint for the design of an epitope-focused vaccination strategy against pathogens that have eluded traditional vaccine development approaches. Beyond immunogen design, the design strategy presented opens a door for the de novo design of proteins stabilizing complex binding sites, applicable to the design of novel functional proteins with defined structural properties.
REFERENCES
[0091] 1. N. Koga et al., Principles for designing ideal protein structures. Nature 491, 222-227 (2012). [0092] 2. E. Marcos et al., Principles for designing proteins with cavities formed by curved beta sheets. Science 355, 201-206 (2017). [0093] 3. P. S. Huang et al., De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nat Chem Biol 12, 29-34 (2016). [0094] 4. M. Mravic et al., Packing of apolar side chains enables accurate design of highly stable membrane proteins. Science 363, 1418-1423 (2019). [0095] 5. A. Chevalier et al., Massively parallel de novo protein design for targeted therapeutics. Nature 550, 74-79 (2017). [0096] 6. E. Procko et al., A computationally designed inhibitor of an Epstein-Barr viral Bcl-2 protein induces apoptosis in infected cells. Cell 157, 1644-1656 (2014). [0097] 7. S. Berger et al., Computationally designed high specificity inhibitors delineate the roles of BCL2 family proteins in cancer. Elife 5, (2016). [0098] 8. M. L. Azoitei et al., Computation-guided backbone grafting of a discontinuous motif onto a protein scaffold. Science 334, 373-376 (2011). [0099] 9. S. Jones, J. M. Thornton, Principles of protein-protein interactions. Proc Natl Acad Sci USA 93, 13-20 (1996). [0100] 10. N. D. Rubinstein et al., Computational characterization of B-cell epitopes. Mol Immunol 45, 3477-3489 (2008). [0101] 11. B. E. Correia et al., Proof of principle for epitope-focused vaccine design. Nature 507, 201-206 (2014). [0102] 12. J. S. McLellan et al., Structure of RSV fusion glycoprotein trimer bound to a prefusion-specific neutralizing antibody. Science 340, 1113-1117 (2013). [0103] 13. J. S. McLellan et al., Structure of a major antigenic site on the respiratory syncytial virus fusion glycoprotein in complex with neutralizing antibody 101F. J Virol 84, 12236-12244 (2010). [0104] 14. D. Tian et al., Structural basis of respiratory syncytial virus subtype-dependent neutralization by an antibody targeting the fusion glycoprotein. Nat Commun 8, 1877 (2017). [0105] 15. J. S. McLellan et al., Design and characterization of epitope-scaffold immunogens that present the motavizumab epitope from respiratory syncytial virus. J Mol Biol 409, 853-866 (2011). [0106] 16. S. J. Fleishman et al., Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332, 816-821 (2011). [0107] 17. J. Bonet et al., Rosetta FunFolDes—A general framework for the computational design of functional proteins. PLoS Comput Biol 14, e1006623 (2018). [0108] 18. T. J. Brunette et al., Exploring the repeat protein universe through computational protein design. Nature 528, 580-584 (2015). [0109] 19. J. S. McLellan et al., Structure-based design of a fusion glycoprotein vaccine for respiratory syncytial virus. Science 342, 592-598 (2013). [0110] 20. P. Kristensen, G. Winter, Proteolytic selection for protein folding using filamentous bacteriophages. Fold Des 3, 321-328 (1998). [0111] 21. M. D. Finucane, M. Tuna, J. H. Lees, D. N. Woolfson, Core-directed protein design. I.
[0112] An experimental method for selecting stable proteins from combinatorial libraries. Biochemistry 38, 11604-11612 (1999). [0113] 22. A. M. Watkins, P. S. Arora, Anatomy of beta-strands at protein-protein interfaces. ACS Chem Biol 9, 1747-1754 (2014). [0114] 23. F. Sesterhenn et al., Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen. PLoS Biol 17, e3000164 (2019). [0115] 24. D. A. Silva et al., De novo design of potent and selective mimics of IL-2 and IL-15. Nature 565, 186-191 (2019). [0116] 25. E. M. Strauch et al., Computational design of trimeric influenza-neutralizing proteins targeting the hemagglutinin receptor binding site. Nat Biotechnol 35, 667-671 (2017). [0117] 26. D. Angeletti et al., Defining B cell immunodominance to viruses. Nat Immunol 18, 456-463 (2017). [0118] 27. D. Corti et al., A neutralizing antibody selected from plasma cells that binds to group 1 and group 2 influenza A hemagglutinins. Science 333, 850-856 (2011). [0119] 28. J. Lee et al., Persistent Antibody Clonotypes Dominate the Serum Response to Influenza over Multiple Years and Repeated Vaccinations. Cell Host Microbe 25, 367-376 e365 (2019). [0120] 29. H. F. Moffett et al., B cells engineered to express pathogen-specific antibodies protect against infection. Sci Immunol 4, (2019).
METHODS
Computational Design of Template-Based Epitope-Focused Immunogens
Site 0
[0121] The structural segments entailing the antigenic site 0 were extracted from the prefusion stabilized RSVF Ds-Cav1 crystal structure, bound to the antibody D25 (PDB ID: 4JHVV) (1). The epitope consists of two segments: a kinked helical segment (residues 196-212) and a 7-residue loop (residues 63-69).
[0122] The MASTER software (2) was used to perform structural searches over the Protein Data Bank (PDB, from August 2018), containing 141,920 protein structures, to select template scaffolds with local structural similarities to the site 0 motif. A first search with a Cα RMSD threshold below 2.5 Å did not produce any usable structural matches both in terms of local mimicry as well as global topology features. A second search was performed, where extra structural elements that support the epitope in its native environment were included as part of the query motif to bias the search towards matches that favoured motif-compatible topologies rather than those with close local similarities. The extra structural elements included were the two buried helices that directly contact the site 0 in the preRSVF structure (4JHW residues 70-88 and 212-229). The search yielded initially 7,600 matches under 5 Å of backbone RMSD, which were subsequently filtered for proteins with a length between 50 and 160 residues, high secondary structure content, as well as for accessibility of the epitope for antibody binding. Remaining matches were manually inspected to select template-scaffolds suitable to present the native conformation of antigenic site 0. Subsequently, we selected a computationally designed, highly stable, helical repeat protein (3) consisting of 8 regular helices (PDB ID: SCWJ) with an RMSD of 4.4 Å to the query (2.82 A for site 0 segments only). To avoid steric clashes with the D25 antibody, we truncated the SCWJ template structure at the N-terminus by 29 residues, resulting in a structural topology composed of 7 helices.
[0123] Using Rosetta FunFolDes (4) the truncated SCWJ topology was folded and designed to stabilize the grafted site 0 epitope recognized by D25. We generated 25,000 designs and selected the top 300 by Rosetta energy score (RE), designed backbones that presented obvious flaws, as low packing scores, distorted secondary structural elements and buried unsatisfied atoms were discarded. From the top 300 designs, 3 were retained for follow-up iterative cycles of structural relaxation and design using Rosetta FastDesign (5), generating a total of 100 designed sequences.
[0124] The best 9 designs by Rosetta energy score were recombinantly expressed in E. coli. 2 designed sequences derived from the same backbone, were successfully expressed and purified. The best variant was named S0_1.1, and subjected to experimental optimization using yeast surface display (
Site IV
[0125] When the design simulations were carried out, there was no structure available of the full RSVF protein in complex with a site IV-specific nAb, nevertheless a peptide epitope of this site recognized by the 101F nAb had been previously reported (PDB ID: 3O41) (7).
[0126] The crystallized peptide-epitope corresponds to the residues 429-434 of the RSVF protein. Structurally the 101F-bound peptide-epitope adopts a bulged strand and several studies suggest that 101F recognition extends beyond the linear β-strand, contacting other residues located in antigenic site IV (8). Despite the apparent structural simplicity of the epitope, structural searches for designable scaffolds failed to yield promising starting templates. However, we noticed that the antigenic site IV of RSVF is self-contained within an individual domain that could potentially be excised and designed as a soluble folded protein. To maximize these contacts, we first truncated the seemingly self-contained region from RSVF pre-fusion structure (PDB ID: 4JHW, residue: 402-459) forming a β-sandwich and containing site IV. We used Rosetta FastDesign to optimize the core positions of this minimal topology, obtaining our initial design: S4_wt. However, S4_wt did not show a funnel-shaped energy landscape in Rosetta ab initio simulations, and we were unable to obtain expression in E. coli.
[0127] In an attempt to improve the conformation and stabilization of S4_wt, we used Rosetta FunFolDes to fold and design this topology, while keeping the conformation of the site IV epitope fixed. Out of 25,000 simulations, the top 1% decoys according to RE score and overall RMSD were selected for manual inspection, and 12 designed sequences were selected for recombinant expression in E. coli.
TopoBuilder—Motif-Centric De Novo Design
[0128] Given the limited availability of suitable starting templates to host structurally complex motifs such as site 0 and site IV, we developed a template-free design protocol, which we named
[0129] TopoBuilder. In contrast to adapting an existing topology to accommodate the epitope, the design goal is to build protein scaffolds around the epitope from scratch, using idealized secondary structures (beta strands and alpha helices). The length, orientation and 3D-positioning are defined by the user for each secondary structure with respect to the epitope, which is extracted from its native environment. The topologies built were designed to meet the following criteria: (1) Small, globular proteins with a high contact order between secondary structures and the epitope, to allow for stable folding and accurate stabilization of the epitope in its native conformation (2) Context mimicry, i.e. respecting shape constraints of the epitope in its native context (
[0130] For site 0, the short helix of S0_1.39 preceding the epitope loop segment was kept, and a third helix was placed on the backside of the epitope to: (1) provide a core to the protein and (2) allow for the proper connectivity between the secondary structures.
[0131] A total of three different orientations (45°, 0° and −45° degrees to the plane formed by site 0) were tested for the designed supporting alpha helix (
[0132] In the case of site IV, the known binding region to 101F (residues 428F-434F) was extracted from prefusion RSVF (PDB 4JWH). Three antiparallel beta strands, pairing with the epitope, plus an alpha helix on the buried side, were assembled around the 101F epitope. Three different configurations (45°, (−45°, 0°, 10°) and −45° degrees with respect to the β-sheet) were sampled parametrically for the alpha helix (
[0133] The structural sketches were used to generate C! distance constraints to guide Rosetta FunFolDes (4) folding trajectories. Around 25,000 trajectories were generated for each sketch. The newly generated backbones were further subjected to layer-based FastDesign (5), meaning that each amino acid position was assigned a layer (combining ‘core’, ‘boundary’, ‘surface’ and ‘sheet’ or ‘helix’) on the basis of its exposure and secondary structure type, that dictated the allowed amino acid types at that position.
[0134] After iterative cycles of sequence design, unconstraint FastRelax (9) (i.e sidechain repacking and backbone minimization) was applied over the designs to evaluate their conformational stability of the epitope region. After each relax cycle, structural changes of the epitope region were evaluated (epitope RMSD drift). Designs with epitope RMSD drifts higher than 1.2 Å were discarded. Designs were also ranked and selected according hydrophobic core packing (packstat score), with a cutoff of 0.5 for site 0 and 0.6 for the site IV design series, and a cavity volume of <50 Å.sup.3. Between 1,000 and 10,000 of the designed sequences were generated from this computational protocol. We evaluated sequence profiles for the designs, and encoded the critical positions combinatorially by assembling overlapping oligos. Upon PCR assembly, libraries were transformed in yeast and screened for antibody binding and stability as assessed by protease digestion assays (10-12).
Mouse Immunizations
[0135] All animal experiments were approved by the Veterinary Authority of the Canton of Vaud (Switzerland) according to Swiss regulations of animal welfare (animal protocol number 3074). Female Balb/c mice (6-week old) were purchased from Janvier labs. Immunogens were thawed on ice, mixed with equal volumes of adjuvant (2% Alhydrogel, Invivogen or Sigma Adjuvant System, Sigma) and incubated for 30 minutes. Mice were injected subcutaneously with 100 μl vaccine formulation, containing in total 10 μg of immunogen (equimolar ratios of each immunogen for Trivax immunizations). Immunizations were performed on day 0, 21 and 42. 100-200 μl blood were drawn on day 0, 14 and 35. Mice were euthanized at day 56 and blood was taken by cardiac puncture.
NHP Immunizations
[0136] Twenty-one african green monkeys (AGM, 3-4 years) were divided into three experimental groups with at least two animals of each sex. AGMs were pre-screened as seronegative against prefusion RSVF (preRSVF) by ELISA. Vaccines were prepared 1 hour before injection, containing 50 μg preRSVF or 300 μg Trivax1 in 0.5 ml PBS, mixed with 0.5 ml alum adjuvant (Alhydrogel, Invivogen) for each animal. AGMs were immunized intramuscularly at day 0, 28, 56, and 84. Blood was drawn at days 14, 28, 35, 56, 63, 84, 91, 105 and 119.
RSV Neutralization Assay
[0137] The RSV neutralization assay was performed as described previously (13). Briefly, Hep2 cells were seeded in Corning 96-well tissue culture plates (Sigma) at a density of 40,000 cells/well in 100 μl of Minimum Essential Medium (MEM, Gibco) supplemented with 10% FBS (Gibco), L-glutamine 2 mM (Gibco) and penicillin-streptomycin (Gibco), and grown overnight at 37° C. with 5% CO2. Serial dilutions of heat-inactivated sera were prepared in MEM without phenol red (M0, Life Technologies, supplemented with 2 mM L-glutamine and penicillin/streptomycin) and were incubated with 800 pfu/well (final MOI 0.01) RSV-Luc (A2 strain carrying a luciferase gene) for 1 hour at 37° C. Serum-virus mixture was added to Hep2 cell layer, and incubated for 48 hours. Cells were lysed in lysis buffer supplemented with 1 μg/ml luciferin (Sigma) and 2 mM ATP (Sigma), and luminescence signal was read on a Tecan Infinite 500 plate reader. The neutralization curve was plotted and fitted using the GraphPad variable slope fitting model, weighted by 1/Y.sup.2.
Serum Fractionation
[0138] Monomeric Trivax1 immunogens (S2_1, S0_1.39 and S4_1.5) were used to deplete the site 0, II and IV specific antibodies in immunized sera. HisPurTM Ni-NTA resin slurry (Thermo Scientific) was washed with PBS containing 10 mM imidazole. Approximately 1 mg of each immunogen was immobilized on Ni-NTA resin, followed by two wash steps to remove unbound scaffold. 60 μl of sera pooled from all animals within the same group were diluted to a final volume of 600 μl in wash buffer, and incubated overnight at 4° C. with 500 μl Ni-NTA resin slurry. As control, the same amount of sera was incubated with Ni-NTA resin that did not contain scaffolds. Resin was pelleted down at 13,000 rpm for 5 minutes, and the supernatant (depleted sera) was collected and then used for neutralization assays.
Site Saturation Mutagenesis Library (SSM)
[0139] A SSM library was assembled by overhang PCR, in which 11 selected positions surrounding the epitope in the S4_1.1 design model were allowed to mutate to all 20 amino acids, with one mutation allowed at a time. Each of the 11 libraries was assembled by primers (Table 1) containing the degenerate codon ‘NNK’ at the selected position. All 11 libraries were pooled, and transformed into EBY-100 yeast strain with a transformation efficiency of 1×10.sup.6 transformants.
Combinatorial Library
[0140] Combinatorial sequence libraries were constructed by assembling multiple overlapping primers (Table 2) containing degenerate codons at selected positions for combinatorial sampling of hydrophobic amino acids in the protein core. The theoretical diversity was between 1×10.sup.6 and 5×10.sup.6. Primers were mixed (10 μM each), and assembled in a PCR reaction (55° C. annealing for 30 sec, 72° C. extension time for 1 min, 25 cycles). To amplify full-length assembled products, a second PCR reaction was performed, with forward and reverse primers specific for the full-length product. The PCR product was desalted, and transformed into EBY-100 yeast strain with a transformation efficiency of at least 1×10.sup.7 transformants (14).
Protein Expression and Purification
Designed Scaffolds
[0141] All genes of designed proteins were purchased as DNA fragments from Twist Bioscience, and cloned via Gibson assembly into either pET11b or pET21b bacterial expression vectors. Plasmids were transformed into E. coli BL21 (DE3) (Merck) and grown overnight in LB media. For protein expression, precultures were diluted 1:100 and grown at 37° C. until the OD.sub.600 reached 0.6, followed by the addition of 1 mM IPTG to induce expression. Cultures were harvested after 12-16 hours at 22° C. Pellets were resuspended in lysis buffer (50 mM Tris, pH 7.5, 500 mM NaCl, 5% Glycerol, 1 mg/ml lysozyme, 1 mM PMSF, 1 μg/ml DNase) and sonicated on ice for a total of 12 minutes, in intervals of 15 seconds sonication followed by 45 seconds pause. Lysates were clarified by centrifugation (20,000 rpm, 20 minutes) and purified via Ni-NTA affinity chromatography followed by size exclusion on a HiLoad 16/600 Superdex 75 column (GE Healthcare) in PBS buffer.
Antibodies—IgG and Fab Constructs
[0142] Plasmids encoding cDNAs for the heavy chain of IgG were purchased from Genscript and cloned into the pFUSE-CHIg-hG1 vector (Invivogen). Heavy and light chain DNA sequences of antibody fragments (Fab) were purchased from Twist Bioscience and cloned separately into the pHLsec mammalian expression vector (Addgene, #99845) via Gibson assembly. HEK293-F cells were transfected in a 1:1 ratio with heavy and light chains, and cultured in FreeStyle medium (Gibco) for 7 days. Supernatants were collected by centrifugation and purified using a 1 ml HiTrap Protein A HP column (GE Healthcare) for IgG expression and 5 ml kappa-select column (GE Healthcare) for Fab purification. Bound antibodies/Fabs were eluted with 0.1 M glycine buffer (pH 2.7), immediately neutralized by 1 M Tris ethylamine buffer (pH 9), and buffer exchanged to PBS.
Prefusion Stabilized RSVF
[0143] The construct encoding the thermostabilized the preRSVF protein corresponds to the sc9-10 DS-Cav1 A149C Y458C S46G E92D S215P K465Q variant (referred to as DS2) reported by Joyce and colleagues (15). The sequence was codon-optimized for mammalian cell expression and cloned into the pHCMV-1 vector flanked with two C-terminal Strep-Tag II and one 8x His tag. Expression and purification were performed as described previously (13).
Nanoring-Based Immunogens
[0144] The full-length N gene derived from the human RSV strain Long, ATCC VR-26 (GenBank accession number AY911262.1) was PCR amplified and cloned into pET28a+ at Ncol-Xhol sites to obtain the pET-N plasmid. Immunogens presenting sites 0, II and IV epitopes were cloned into the pET-N plasmid at Ncol site as pET-S0_1.39-N, pET-S2_1.2-N and pET-S4_1.5-N, respectively. Expression and purification of the nanoring fusion proteins was performed as described previously (13).
Ferritin-Based Immunogens
[0145] The gene encoding Helicobacter pylori ferritin (GenBank ID: QAB33511.1) was cloned into the pHLsec vector for mammalian expression, with an N-terminal 6x His Tag. The sequence of the designed immunogens (S0_2.126 and S4_2.45) were cloned upstream of the ferritin gene, spaced by a GGGGS linker. Ferritin particulate immunogens were produced by co-transfecting a 1:1 stochiometric ratio of “naked” ferritin and immunogen-ferritin in HEK-293F cells, as previously described for other immunogen-nanoparticle fusion constructs (16). The supernatant was collected 7-days post transfection and purified via Ni-NTA affinity chromatography and size exclusion on a Superose 6 increase 10/300 GL column (GE).
Negative-Stain Transmission Electron Microscopy
Sample Preparation
[0146] RSVN and Ferritin-based nanoparticles were diluted to a concentration of 0.015 mg/ml. The samples were absorbed on carbon-coated copper grid (EMS, Hatfield, Pa., United States) for 3 mins, washed with deionized water and stained with freshly prepared 0.75% uranyl formate.
Data Acquisition
[0147] The samples were viewed under an F20 electron microscope (Thermo Fisher) operated at 200 kV. Digital images were collected using a direct detector camera Falcon III (Thermo Fisher) with the set-up of 4098×4098 pixels. The homogeneity and coverage of staining samples on the grid was first visualized at low magnification mode before automatic data collection. Automatic data collection was performed using EPU software (Thermo Fisher) at a nominal magnification of 50,000×, corresponding to pixel size of 2 Å, and defocus range from −1 μm to −2 μm.
Image Processing
[0148] CTFFIND4 program (17) was used to estimate the contrast transfer function for each collected image. Around 1000 particles were manually selected using the installed package XMIPP within SCIPION framework (18). Manually picked particles were served as input for XMIPP auto-picking utility, resulting in at least 10,000 particles. Selected particles were extracted with the box size of 100 pixels and subjected for three rounds of reference-free 2D classification without CTF correction using RELION-3.0 Beta suite (19).
RSVF-Fabs Complex Formation and Negative Stain EM
[0149] 20 μg of RSVF trimer was incubated overnight at 4° C. with 80 μg of Fabs (Motavizumab, D25 or 101F). For complex formation with all three monoclonal Fabs, 80 μg of each Fab was used. Complexes were purified on a Superose 6 Increase 10/300 column using an Akta Pure system (GE Healthcare) in TBS buffer. The main fraction containing the complex was directly used for negative stain EM. Purified complexes of RSVF and Fabs were deposited at approximately 0.02 mg/ml onto carbon-coated copper grids and stained with 2% uranyl formate. Images were collected with a field-emission FEI Tecnai F20 electron microscope operating at 200 kV. Images were acquired with an Orius charge-coupled device (CCD) camera (Gatan Inc.) at a calibrated magnification of ×34,483, resulting in a pixel size of 2.71 A. For the complexes of RSVF with a single Fab, approximately 2,000 particles were manually selected with Cryosparc2 (20). Two rounds of 2D classification of particle images were performed with 20 classes allowed. For the complexes of RSVF with D25, Motavizumab and 101F Fabs, approximately 330,000 particles were picked using Relion 3.0 (19) and subsequently imported to Cryosparc2 for two rounds of 2D classification with 50 classes allowed.
Determining Binding Affinities by Surface Plasmon Resonance (SPR)
[0150] SPR measurements were performed on a Biacore 8K (GE Healthcare) with HBS-EP+ as running buffer (10 mM HEPES pH 7.4, 150 mM NaCl, 3 mM EDTA, 0.005% v/v Surfactant
[0151] P20, GE Healthcare). Ligands were immobilized on a CM5 chip (GE Healthcare #29104988) via amine coupling. Approximately 2000 response units (RU) of IgG were immobilized, and designed monomeric proteins were injected as analyte in two-fold serial dilutions. The flow rate was 30 μl/min for a contact time of 120 seconds followed by 400 seconds dissociation time. After each injection, surface was regenerated using 3 M magnesium chloride (101F as immobilized ligand) or 0.1 M Glycine at pH 4.0 (Motavizumab and D25 IgG as an immobilized ligand). Data were fitted using 1:1 Langmuir binding model within the Biacore 8K analysis software (GE Healthcare #29310604).
Dissection of Serum Antibody Specificities by SPR
[0152] To quantify the epitope-specific antibody responses in bulk serum from immunized animals, we performed an SPR competition assay with the monoclonal antibodies (D25, Motavizumab and 101F) as described previously (13). Briefly, approximately 400 RU of prefusion RSVF were immobilized on a CM5 chip via amine coupling, and serum diluted 1:10 in running buffer was injected to measure the total response ((RU.sub.non-blocked surface). After chip regeneration using 50 mM NaOH, the site 0/II/IV epitopes were blocked by injecting saturating amounts of either D25, Motavizumab, or 101F IgG, and serum was injected again to quantify residual response (RU.sub.blocked surface). The delta serum response (ΔSR) was calculated as follows:
ΔSR=RU.sub.(non-)blocked surface−RU.sub.Baseline
[0153] Percent blocking was calculated for each site as:
SEC-MALS
[0154] Size exclusion chromatography with an online multi-angle light scattering (MALS) device (miniDAWN TREOS, Wyatt) was used to determine the oligomeric state and molecular weight for the protein in solution. Purified proteins were concentrated to 1 mg/ml in PBS (pH 7.4), and 100 μl of sample was injected into a Superdex 75 300/10 GL column (GE Healthcare) with a flow rate of 0.5 ml/min, and UV280 and light scattering signals were recorded. Molecular weight was determined using the ASTRA software (version 6.1, Wyatt).
Circular Dichroism
[0155] Far-UV circular dichroism spectra were measured using a Jasco-815 spectrometer in a 1 mm path-length cuvette. The protein samples were prepared in 10 mM sodium phosphate buffer at a protein concentration of 30 μM. Wavelengths between 190 nm and 250 nm were recorded with a scanning speed of 20 nm min.sup.−1 and a response time of 0.125 sec. All spectra were averaged 2 times and corrected for buffer absorption. Temperature ramping melts were performed from 25 to 90° C. with an increment of 2° C./min in presence or absence of 2.5 mM TCEP reducing agent. Thermal denaturation curves were plotted by the change of ellipticity at the global curve minimum to calculate the melting temperature (T.sub.m).
Yeast Surface Display
[0156] Libraries of linear DNA fragments encoding variants of the designed proteins were transformed together with linearized pCTcon2 vector (Addgene #41843) based on the protocol previously described by Chao and colleagues (14). Transformation procedures generally yielded ˜10.sup.7 transformants. The transformed cells were passaged twice in SDCAA medium before induction. To induce cell surface expression, cells were centrifuged at 7,000 r.p.m. for 1 min, washed with induction media (SGCAA) and resuspended in 100 ml SGCAA with a cell density of 1×10.sup.7 cells/ml SGCAA. Cells were grown overnight at 30° C. in SGCAA medium. Induced cells were washed in cold wash buffer (PBS+0.05% BSA) and labelled with various concentration of target IgG or Fab (101F, D25, and 5C4) at 4° C. After one hour of incubation, cells were washed twice with wash buffer and then incubated with FITC-conjugated anti-cMyc antibody and PE-conjugated anti-human Fc (BioLegend, #342303) or PE-conjugated anti-Fab (Thermo Scientific, #MA1-10377) for an additional 30 min. Cells were washed and sorted using a SONY SH800 flow cytometer in ‘ultra-purity’ mode. The sorted cells were recovered in SDCAA medium, and grown for 1-2 days at 30° C. In order to select stably folded proteins, we washed the induced cells with TBS buffer (20 mM Tris, 100 mM NaCl, pH 8.0) three times and resuspended in 0.5 ml of TBS buffer containing 1 μM of chymotrypsin. After incubating five-minutes at 30° C., the reaction was quenched by adding 1 ml of wash buffer, followed by five wash steps. Cells were then labelled with primary and secondary antibodies as described above.
ELISA
[0157] 96-well plates (Nunc MediSorp platesf Thermo Scientific) were coated overnight at 4° C. with 50 ng/well of purified antigen (recombinant RSVF or designed immunogen) in coating buffer (100 mM sodium bicarbonate, pH 9) in 100 μl total volume. Following overnight incubation, wells were blocked with blocking buffer (PBS +0.05% Tween 20 (PBST) containing 5% skim milk (Sigma)) for 2 hours at room temperature. Plates were washed five times with PBST. 3-fold serial dilutions were prepared and added to the plates in duplicates, and incubated at room temperature for 1 hour. After washing, anti-mouse (abcam, #99617) or anti-monkey (abcam, #112767) HRP-conjugated secondary antibody were diluted 1:1,500 or 1:10,000, respectively, in blocking buffer and incubated for 1 hour. An additional five wash steps were performed before adding 100 μl/well Pierce TMB substrate (Thermo Scientific). The reaction was terminated by adding an equal volume of 2 M sulfuric acid. The absorbance at 450 nm was measured on a Tecan Safire 2 plate reader, and the antigen specific titers were determined as the reciprocal of the serum dilution yielding a signal two-fold above the background.
NMR
[0158] Protein samples for NMR were prepared in 10 mM sodium phosphate buffer, 50 mM sodium chloride at pH 7.4 with the protein concentration of 500 μM. All NMR experiments were carried out in a 18.8T (800 MHz proton Larmor frequency) Bruker spectrometer equipped with a CPTC 1H,.sup.13C,.sup.15N 5 mm cryoprobe and an Avance III console. Experiments for backbone resonance assignment consisted in standard triple resonance spectra HNCA, HN(CO)CA, HNCO, HN(CO)CA, CBCA(CO)NH and HNCACB acquired on a 0.5 mM sample doubly labelled with .sup.13C and .sup.15N (21). Sidechain assignments were obtained from HCCH-TOCSY experiments acquired on the same sample plus HNHA, NOESY-.sup.15N-HSQC and TOCSY-.sup.15N-HSQC acquired on a .sup.15N-labeled sample. The NOESY-.sup.15N-HSQC was used together with a 2D NOESY collected on an unlabelled sample for structure calculations.
[0159] Spectra for backbone assignments were acquired with 40 increments in the .sup.15N dimension and 128 increments in the .sup.13C dimension, and processed with 128 and 256 points by using linear prediction. HCCH-TOCSY were recorded with 64-128 increments in the .sup.13C dimensions and processed with twice the number of points. .sup.15N-resolved NOESY and TOCSY spectra were acquired with 64 increments in .sup.15N dimension and 128 in the indirect .sup.1H dimension, and processed with twice the number of points. .sup.1H-.sup.1H 2D-NOESY and 2D TOCSY spectra were acquired with 256 increments in the indirect dimension, processed with 512 points. Mixing times for NOESY spectra were 100 ms and TOCSY spin locks were 60 ms. Heteronuclear .sup.1H-.sup.15N NOE was measured with 128 .sup.15N increments processed with 256 points, using 64 scans and a saturation time of 6 seconds. All samples were prepared in 20 mM phosphate buffer pH 7, with 10% .sup.2H.sub.2O and 0.2% sodium azide to prevent sample degradation.
[0160] All spectra were acquired and processed with Bruker's TopSpin 3.0 (acquisition with standard pulse programs) and analyzed manually with the program CARA (http://cara.nmr.ch/doku.php/home) to obtain backbone and sidechain resonance assignments. Peak picking and assignment of NOESY spectra (a .sup.15N-resolved NOESY and a 2D NOESY) were performed automatically with the program UNIO-ATNOS/CANDID (22, 23) coupled to Cyana 2.1 (24), using standard settings in both programs. The run was complemented with dihedral angles derived from chemical shifts with Talos-n (25).
X-Ray Crystallization and Structural Determination
Co-Crystallization of Complex D25 Fab with S0 2.126
[0161] After overnight incubation at 4° C., the S0_2.126/D25 Fab complex was purified by size exclusion chromatography using a Superdex200 26 600 (GE Healthcare) equilibrated in 10 mM Tris pH 8, 100 mM NaCl and subsequently concentrated to ˜10 mg/ml (Amicon Ultra-15, MWCO 3,000). Crystals were grown at 291K using the sitting-drop vapor-diffusion method in drops containing 1 μl purified protein mixed with 1 μl reservoir solution containing 10% PEG 8000, 100 mM HEPES pH 7.5, and 200 mM calcium acetate.
[0162] For cryo protection, crystals were briefly swished through mother liquor containing 20% ethylene glycol.
Data Collection and Structural Determination of the S0 2.126/D25 Fab Complex
[0163] Diffraction data was recorded at ESRF beamline ID30B. Data integration was performed by XDS (26) and a high-resolution cut at I/σ=1 was applied. The dataset contained a strong off-origin peak in the Patterson function (88% height rel. to origin) corresponding to a pseudo translational symmetry of 1/2, 0, 1/2. The structure was determined by the molecular replacement method using PHASER (27) using the D25 structure (1) (PDB ID 4JHVV) as a search model. Manual model building was performed using Coot (28), and automated refinement in Phenix (29). After several rounds of automated refinement and manual building, paired refinement (30) determined the resolution cut-off for final refinement.
Co-Crystallization of Complex 101F Fab with S4 2.45
[0164] The complex of S4_2.45 with the F101 Fab was prepared by mixing two proteins in 2:1 molar ratio for 1 hour at 4° C., followed by size exclusion chromatography using a Superdex-75 column. Complexes of S4_2.45 with the 101F Fab were verified by SDS-PAGE. Complexes were subsequently concentrated to 6-8 mg/ml. Crystals were grown using hanging drops vapor-diffusion method at 20° C. The S4_2.45/101F protein complex was mixed with equal volume of a well solution containing 0.2 M Magnesium acetate, 0.1 M Sodium cacodylate pH 6.5, 20%(w/v) PEG 8000. Native crystals were transferred to a cryoprotectant solution of 0.2 M Magnesium acetate, 0.1 M Sodium cacodylate pH 6.5, 20% (w/v) PEG 8000 and 15% glycerol, followed by flash-cooling in liquid nitrogen.
Data Collection and Structural Determination of the S4 2.45/101F Fab Complex
[0165] Diffraction data were collected at SSRL facility, BL9-2 beamline at the SLAC National Accelerator Laboratory. The crystals belonged to space group P3221. The diffraction data were initially processed to 2.6 Å with X-ray Detector Software (XDS) (Table 9). Molecular replacement searches were conducted with the program PHENIX PHASER using 101F Fab model (PDB ID: 3041) and S4_2.45/101F Fab computational model generated from superimposing epitope region of S4_2.45 with the peptide-bound structure (PDB ID: 3041), and yielded clear molecular replacement solutions. Initial refinement provided a Rfree of 42.43% and Rwork of 32.25% and a complex structure was refined using Phenix Refine, followed by manual rebuilding with the program COOT. The final refinement statistics, native data and phasing statistics are summarized in Table 9.
Next-Generation Sequencing of Design Pools
[0166] After sorting, yeast cells were grown overnight, pelleted and plasmid DNA was extracted using Zymoprep Yeast Plasmid Miniprep II (Zymo Research) following the manufacturer's instructions. The coding sequence of the designed variants was amplified using vector-specific primer pairs, Illumina sequencing adapters were attached using overhang PCR, and PCR products were desalted (Qiaquick PCR purification kit, Qiagen). Next generation sequencing was performed using an Illumina MiSeq 2×150 bp paired end sequencing (300 cycles), yielding between 0.45-0.58 million reads/sample.
[0167] For bioinformatic analysis, sequences were translated in the correct reading frame, and enrichment values were computed for each sequence. We defined the enrichment value E as follows:
[0168] The high selective pressure corresponds to low labelling concentration of the respective target antibodies (100 μM D25, 10 nM 5C4 or 20 μM 101F, as shown in
TABLES
[0169]
TABLE-US-00001 TABLE 1 Primers used for constructing single > site saturation mutagenesis library for S4_1 design. S4_1_SSM_fw CAGGCTAGTGGTGGAGGAGGCTCTGGTGGAGGCGGTAGCGGAGGC (SEQ ID NO: GGAGGGTCGGCTAGC 2) S4_1_SSM_rw CTGTTGTTATCAGATCTCGAGCTATTACAAGTCCTCTTCAGAAATA (SEQ ID NO: AGCTTTTGTTCGGATCC 3) S4_1_#18_rw TTTCGGGCATTTGACTTTGATACCATTGCTGT (SEQ ID NO: 4) S4_1_#18_fw CAATGGTATCAAAGTCAAATGCCCGAAANNKGGTGAATGTACGAT (SEQ ID NO: TAAAGACAGTCAACG 5) S4_1_#20_rw CTTTCGGGCATTTGACTTTGATACCATTGCTGT (SEQ ID NO: 6) S4_1_#20_fw GCAATGGTATCAAAGTCAAATGCCCGAAAGGCGGTNNKTGTACGA (SEQ ID NO: TTAAAGACAGTCAACGTGG 7) S4_1_#22_rw CCTTTCGGGCATTTGACTTTGATACCATTGCTGT (SEQ ID NO: 8) S4_1_#22_fw GCAATGGTATCAAAGTCAAATGCCCGAAAGGCGGTGAATGTNNKA (SEQ ID NO: TTAAAGACAGTCAACGTGGCATTATC 9) S4_1_#25_rw TTTAATCGTACATTCACCGCCTTTCG (SEQ ID NO: 10) S4_1_#25_fw CGAAAGGCGGTGAATGTACGATTAAANNKAGTCAACGTGGCATTA (SEQ ID NO: TCAAAACC 11) S4_1_#36_rw GCTAAAGGTTTTGATAATGCCACGTTGAC (SEQ ID NO: 12) S4_1_#36_fw CAACGTGGCATTATCAAAACCTTTAGCNNKGGTACGGAAGAAGTT (SEQ ID NO: CGCAGTC 13) S4 1 #39 rw CGTACCAGAGCTAAAGGTTTTGATAATGCCA (SEQ ID NO: 14) S4_1_#39_fw GCATTATCAAAACCTTTAGCTCTGGTACGNNKGAAGTTCGCAGTCC (SEQ ID NO: GTCCCTG 15) S4 1 #43 rw GCGAACTTCTTCCGTACCAGAGCTAAAG (SEQ ID NO: 16) S4_1_#43_fw GCTCTGGTACGGAAGAAGTTCGCNNKCCGTCCCTGGGCAAAGTGA (SEQ ID NO: CCGT 17) S4_1_#45_fw GCTCTGGTACGGAAGAAGTTCGCAGTCCGNNKCTGGGCAAAGTGA (SEQ ID NO: CCGTTGGTGATAAC 18) S4_1_#48 rw GCCCAGGGACGGACTGCGAACTTC (SEQ ID NO: 19) S4_1_#48 fw GTTCGCAGTCCGTCCCTGGGCNNKGTGACCGTTGGTGATAACACGT (SEQ ID NO: TC 20) S4_1_#50 fw GTTCGCAGTCCGTCCCTGGGCAAAGTGNNKGTTGGTGATAACACGT (SEQ ID NO: TCGAAGCG 21)
TABLE-US-00002 TABLE 2 Primers used for encoding computationally designed sequences of S4_2 design series. S4_2_uni_O1 GACAATAGCTCGACGATTGAAGGTAGATACCCATACGACGTTCCA (SEQ ID NO: GACTACGCTCTGCAGGCTAGTGGTGGAGGAGG 22) S4_2_uni_O2 CCCTCCGCCTCCGCTACCGCCTCCACCAGAGCCTCCTCCACCACTA (SEQ ID NO: GCCTG 23) S4_2_bb1_O3.1 GTAGCGGAGGCGGAGGGTCGGCTAGCCATATGCCGTCCATCYACT (SEQ ID NO: CAKWCGTTSYTGGTGGGAACATCAAGGTGAAGTGC 24) S4_2_bb1_O3.2 GTAGCGGAGGCGGAGGGTCGGCTAGCCATATGCCGTCCATCYACT (SEQ ID NO: CAKWCGTTSYTGGGAACATCAAGGTGAAGTGC 25) S4_2_bb1_O3.3 GTAGCGGAGGCGGAGGGTCGGCTAGCCATATGCCGTCCATCYACT (SEQ ID NO: CAKWCGTTSYTAACGGGAACATCAAGGTGAAGTGC 26) S4_2_bb1_O4.1 GGTCTTGATGATGCCACGCTGGCTATCCTCGATGGTACATTTGTCA (SEQ ID NO: CCAGTGCACTTCACCTTGATGTTCCC 27) S4_2_bb1_O4.2 GGTCTTGATGATGCCACGATTCTTGTTCTCGATGGTACATTTGTCA (SEQ ID NO: C CAGTGCACTTCACCTTGATGTTCCC 28) S4_2_bb1_O4.3 GGTCTTGATGATGCCACGCTGGCTATCCTCGATGGTACACTTGCCC (SEQ ID NO: TCGTGGCACTTCACCTTGATGTTCCC 29) S4_2_bb1_O5.1 GCGTGGCATCATCAAGACCACGAATGTTGATATTGCTGAGGAGRY (SEQ ID NO: GYRGAAGCAGSYTCAAGAGBYTBWGGAAGMGAAACGTAAGGGCT 30) CGTGGGGCTCG S4_2_bb1_O5.2 GCGTGGCATCATCAAGACCTTCACGGGGTTCGAGCCCGAGGAGRY (SEQ ID NO: GYRGAAGCAGSYTCAAGAGBYTBWGGAAGMGAAACGTAAGGGCT 31) CGTGGGGCTCG S4_2_bb1_O5.3 GCGTGGCATCATCAAGACCGTCCCGATGATCGAGACAGGGGAGGA (SEQ ID NO: GRYGYRGAAGCAGSYTCAAGAGBYTBWGGAAGMGAAACGTGGCT 32) CGTGGGGCTCG S4_2_uni_O6 CAGAAATAAGCTTTTGTTCGGATCCGGGCTCAGCCTATTAGTGGTG (SEQ ID NO: GTGGTGGTGGTGCGAGCCCCACGAGCC 33) S4_2_uni_O7 GGATCCGAACAAAAGCTTATTTCTGAAGAGGACTTGTAATAGCTCG (SEQ ID NO: AGATCTGATAAC 34) S4_2_uni_O8 GTACGAGCTAAAAGTACAGTGGGAACAAAGTCGATTTTGTTACAT (SEQ ID NO: CTACACTGTTGTTATCAGATCTCGAGCTATTACAAGTCC 35) S4_2_bb2_O3.1 TAGCGGAGGCGGAGGGTCGGCTAGCCATATGCCAAAWACCHACGT (SEQ ID NO: AWTTGAAGCAGGCDTCAGCTTCACCTGCTTAGGTGAGAAGTGCAC 36) CATCGAGGAC S4_2_bb2_O3.2 TAGCGGAGGCGGAGGGTCGGCTAGCCATATGCCAAAWACCHACGT (SEQ ID NO: AWTTCCCTCGDTCAGCTTCACCTGCTTAGGTGAGAAGTGCACCATC 37) GAGGAC S4_2_bb2_O3.3 TAGCGGAGGCGGAGGGTCGGCTAGCCATATGCCAAAWACCHACGT (SEQ ID NO: AWTTCCCTCGDTCAGCTTCACCTGCCCTAAGGGGGGGAAGTGCAC 38) CATCGAGGAC S4_2_bb2_O4.1 CGGTCTTGATGATCCCACGTTGTGAGTCCTCGATGGTGCACTTC (SEQ ID NO: 39) S4_2_bb2_O4.2 CGGTCTTGATGATCCCACGATCGTCCTCGATGGTGCACTTC (SEQ ID NO: 40) S4_2_bb2_O4.3 CGGTCTTGATGATCCCACGCGAGCGGTCCTCGATGGTGCACTTC (SEQ ID NO: 41) S4_2_bb2_O5.1 CGTGGGATCATCAAGACCGGCAAAAATGCCGAGGAGKYCDKGGA (SEQ ID NO: AGATBTCGAGAAGVRGGHGCGTGCCCAGGGCTCGTGGGGCTCGCA 42) C S4_2_bb2_O5.2 CGTGGGATCATCAAGACCGGCACACATCCAGAGGAGKYCDKGGAA (SEQ ID NO: GATBTCGAGAAGVRGGHGCGTGCCCAGGGCTCGTGGGGCTCGCAC 43) S4_2_bb2_O5.3 CGTGGGATCATCAAGACCGGCAAAAATAAGGAGGAGKYCDKGGA (SEQ ID NO: AGATBTCGAGAAGVRGGHGCGTGCCCAGGGCTCGTGGGGCTCGCA 44) C S4_2_bb3_O3.1 TAGCGGAGGCGGAGGGTCGGCTAGCCATATGGTCTKSAGTKKTGT (SEQ ID NO: AGYTGGGGAGAACTATTCARYTAAGTGTACTGGCGACAAGTGCAC 45) CATCGAGGAC S4_2_bb3_O3.2 TAGCGGAGGCGGAGGGTCGGCTAGCCATATGGTCTKSAGTKKTGT (SEQ ID NO: AGYTACCCCGACATTTTCARYTAAGTGTACTGGCGACAAGTGCACC 46) ATCGAGGAC S4_2_bb3_O3.3 TAGCGGAGGCGGAGGGTCGGCTAGCCATATGTKSAGTKKTGTAGY (SEQ ID NO: TGGGGAGAACTATTCARYTAAGTGTCCTAAGGGGGGCAAGTGCAC 47) CATCGAGGAC S4 2 bb3 O4.1 GGTCTTGATGATCCCGCGCTGTGAGTCCTCGATGGTGCACTTG (SEQ ID NO: 48) S4 2 bb3 O4.2 GGTCTTGATGATCCCGCGATTCTTGTCCTCGATGGTGCACTTG (SEQ ID NO: 49) S4 2 bb3 O4.3 GGTCTTGATGATCCCGCGCCCGCCATAGTCCTCGATGGTGCACTTG (SEQ ID NO: 50) S4_2_bb3_O5.1 CGCGGGATCATCAAGACCACGATTGGAGATACATGTGAGSHGKYG (SEQ ID NO: KMTAAGGCGGYTCAAAAGGCTSVGAAAGGCTCGTGGGGCTCG 51) S4_2_bb3_O5.2 CGCGGGATCATCAAGACCGTTACTGGCAGTCGCTGTGAGSHGKYG (SEQ ID NO: KMTAAGGCGGYTCAAAAGGCTSVGAAAGGCTCGTGGGGCTCG 52)
TABLE-US-00003 TABLE 3 Computationally designed protein sequences for S4_1 design series. Design Expression name Sequence vector S4_1.1 MDGTLQINSNGIKVKCPKGGECTIKDSQRGIIKTFSSGTEEV pET21b (SEQ ID RSPS LGKVTVGDNTFEASNGSWLEHHHHHH NO: 53) S4_1.2 MHHHHHHWGSPGTVTLNSNGLTVTGNDNYNLTVTGNDRGIIK pET11b (SEQ ID T FSPSTETTNDDGMSTITVGNLTVTLGN NO: 54) S4_1.3 MHHHHHHWGSQSTVNVQDKNIRIEVDDKNSVQVNGSNRGIIK pET11b (SEQ ID TF SPGTVQISSKNGDTVTVGNVRVNMGG NO: 55) S4_1.4 MHHHHHHWGSQSTVNVQDKNIRIECDDNCGVQVNGSNRGIIK pET11b (SEQ ID T FSPGTVQISSKNGDTVTVGNVRVNMGG NO: 56) S4_1.5 MHHHHHHWGSDGTLQINSHGVKVKAPPGSGATVKDSQRGIIK pET11b (SEQ ID TF SSGYEEVRSPSLGKVTVGDNTFEVSN NO: 57) S4_1.6 MHHHHHHWGSDGTLQINSHGVKVKCPKGSECTVKDSQRGIIK pET11b (SEQ ID TF SSGYEEVRSPSLGKVTVGDNTFEVSN NO: 58) S4_1.7 MHHHHHHGSKVTFRQDKNGIKIRVNGNKGLVIRTNDRGIIKT pET11b (SEQ ID FS NGTYDIPNSGYNRFTVGGTQFDWNE NO: 59) S4_1.8 MHHHHHHGSKVTFRQDKNGIKFRVNGNKGAVIRTNDRGIIKT pET11b (SEQ ID FS NGTYDIPNSGYNRFTVGGNTFDWNE NO: 60) S4_1.9 MDGTLQINSNGVKVKCPKGVECTVKDSQRGIIKTFSSGTEEV pET21b (SEQ ID RSP SLGKVTVGDNTFEVSNGSWLEHHHHHH NO: 61) S4_1.10 MDGTLQINSNGVKVKCPKGAECTVKVSQRGIIKTFSSGTEEV pET21b (SEQ ID RSP SLGKVTVGDNTTEVSNGSWLEHHHHHH NO: 62) S4_1.11 MHHHHHHWGSPGTVKLNSNGLTVRGNDSYGLTVRGNDRGIIK pET11b (SEQ ID T FSPSTEVVQSKGMSTITVGNLDVRLGE NO: 63)
TABLE-US-00004 TABLE 4 Computationally designed protein sequences for S0_1 design series. Design Expression name Sequence vector S0_1.1 PEDAQKEASKGSEVRELKNIIDKQLLPIVNKTSCSGAEQAA pET21 (SEQ ID EAL NO: 64) REALEGAGSCDAVEQLLGNIKEIKCGTDAGRALIRILAEVA REI GCPRAIDQVAEWVRRIAKAVGGEEAKKQVKEVEKEIRREKG S0_1.17 PEDAQKEASKGSEVRELKNIIDKQLLPILNKASCSGAEQLL pET21 (SEQ ID EAL NO: 65) REALEGAGSCDAVEQLLGNIKEIKCGTDAGRALKRILEEVQ REI GCGSW S0_1.37 CDQLKNYIDKQLLPIVNKQSCANGEEALKDIEKALRGAGSK pET21 (SEQ ID DC WKELLSNIKEIKCGKEFAEKLKKEWERIKKEAGD NO: 66) S0_1.38 CDQIKNYIDKQLLPIVNKAGCGSAEEALKDIEKALRLAGSK pET21 (SEQ ID DCL KEIFSNIKEIKCGKEFAEKLKKEWERIKKEAGD NO: 67) S0_1.39 CDQIKNYIDKQLLPIVNKAGCGSAEEVLKDIEKALRNAGSK pET21 (SEQ ID DCL KEIFSGIKEIKCGKEQAEKLKKEWERIKKEAGDG NO: 68) S0_1.40 ADQIKNYIDKQLLPIVNKAGCGSAEEVLKDIEKALRNAGSK pET21 (SEQ ID DA LKEIFSGIKEIKCGKEQAEKLKKEWERIKKEAGDG NO: 69)
TABLE-US-00005 TABLE 5 Protein sequences for S4_2 design series. Successful Design name Sequence expression S4_2.07 MCSVVVGENYSIKCNPDGKCTIEDKNRGIIKTV yes (SEQ ID NO: TGSRCELLYKAVQ KAQKGSWGSHHHHHH 70) S4_2.19 MPNTNVFPSFSFTCLPDGKCIIEDSQRGIIKTG yes (SEQ ID NO: KNKEEFMEDFEKQV RAQGSWGSHHHHHH 71) S4_2.20 MPSIYSDVPGGNIKVKCHEGKCTIEDSQRGIIK yes (SEQ ID NO: TVPMIETGEEMWK 72) QVQEVLEEKRGSWGSHHHHHH S4_2.21 MPKTNVIPSFSFTCLGEKCTIEDSQRGIIKTGK yes (SEQ ID NO: NKEEVLEDFEKEER AQGSWSHHHHHH 73) S4_2.22 MPSIYSDVPGNIKVKCHEGKCTIEDSQRGIIKT yes (SEQ ID NO: VPMIETGEEMWKQ 74) PQELLEEKRGSWGSHHHHHH S4_2.35 MPNTNVFPSFSFTCLPDGKCIIEDSQRGIIKTG yes (SEQ ID NO: KNKEEFMEDFEKKV RAQGSWGSHHHHHH 75) S4_2.45 MVCSVVVGENYSIKCDATKCTIEDKNRGIIKTV yes (SEQ ID NO: TGSRCEELAKAV QKAQKGSWGSHHHHHH 76) S4_2.60 MPSIYSDVPGGNVKVKCHEGKCTIEDSQRGIIK yes (SEQ ID NO: TVPMIETGEEMWK 77) QVQEVVEEKRGSWGSHHHHHH S4_2.68 MPSIHSVVVGGNIKVKCHEGKCTIEDSQRGIIK yes (SEQ ID NO: TVPMIETGEEMQK 78) QVQEFLEAKRGSWGSHHHHHH S4_2.73 MVCSVVVGENYSIKCDATKCTIEDSQRGIIKTG yes (SEQ ID NO: THPEEFLEDLEKK ARAQGSWGSHHHHHH 79) S4_2.74 MVFSCVVGENYSIKCDATKCTIEDSQRGIIKTG yes (SEQ ID NO: THPEEFLEDLEKK ARAQGSWGSHHHHHH 80) S4_2.84 MPSIHSVVPGGNIKVKCHEGKCTIEDSQRGIIK yes (SEQ ID NO: TVPMIETGEEMWK 81) QPQELLEEKRGSWGSHHHHHH S4_2.85 MPNTNVFPSFSFTCLPDGKCIISDSQRGIIKTG yes (SEQ ID NO: KNKEEFMEDFEKQV RAQGSWGSHHHHHH 82) S4_2.94 HMPSIHSVVAGGNIKVKCHEGKCTIEDSQRGII yes (SEQ ID NO: KTFTGFEPEEVWK 83) QAQEFLEEKRGSWGSHHHHH
TABLE-US-00006 TABLE 6 Protein sequences for S0_2 design series. Successful Design name Sequence expression S0_2.37 MSCDQIKNYIDKQLLPIVNKAGCSRPEELEERI no (SEQ ID NO: RRALKKFGDT 84) DCLKDILLGIKEWKCGGSLEHHHHHH S0_2.79 MPCDKQKNYIDKQLLPIVNKAGCSRPEEVEEMV yes (SEQ ID NO: RRALKKLGE 85) TPCLEDILRGIKEIKCGGSLEHHHHHH S0_2.10 MPCDDAKNYIDKQLLPIVNKAGCSRPEEVERAV yes (SEQ ID NO: RKMLKKMG 86) NTDCLEDILRGIKEIKCGGSLEHHHHHH S0_2.102 MSCDQIKNYIDKQLLPIVNKAGCGSAKEVQKDI no (SEQ ID NO: EKALRNAGV 87) KDCLEDILRGIKEWKCGGSLEHHHHHH S0_2.31 MSCDESKNYIDKQLLPIVNKAGCDRPEDVERWI no (SEQ ID NO: RKALKKMG 88) DTSCFDEILKGLKEIKCGGSLEHHHHHH S0_2.197 MSCDQIKNYIDKQLLPIVNKAGCSRPEEVEERI no (SEQ ID NO: RRALKKMGDT 89) SCFDEIMKGLKEIKCGGSLEHHHHHH S0_2.57516 MSCDQIKNYIDKQLLPIVNKAGCNRPEEFEEWI no (SEQ ID NO: KRALKKLGDT 90) SCLEDILRGIKEIKCGGSLEHHHHHH S0_2.57575 MSCDQIKNYIDKQLLPIVNKAGCSRPEEVEEMV no (SEQ ID NO: RRALKKLGE 91) TPCLEDILRGIKEWKCGGSLEHHHHHH S0_2.57588 MSCDQIKNYIDKQLLPIVNKAGCSRPEEVERAV no (SEQ ID NO: RKMLKKMG 92) NTDCLEDILRGIKEIKCGGSLEHHHHHH S0_2.57855 MSCDQIKNYIDKQLLPIVNKAGCGSAKEVQKDI no (SEQ ID NO: EKALRNAGV 93) KDCLKEIFSGIKEIKCGGSLEHHHHHH S0_2.57910 MSCDQIKNYIDKQLLPIVNKAGCGSAKEVQKDI no (SEQ ID NO: EKALRNAGV 94) KDCLEDILRGIKEIKCGGSLEHHHHHH S0_2.57911 MSCEEAKNYIDKQLLPIVNKAGCGSAEEVQKDI no (SEQ ID NO: EKALRNAGV 95) KDCLEDILRGIKEWKCGGSLEHHHHHH S0_2.57 MPCDDAKNYIDKQLLPIVNKAGCSRPEEVEERI yes (SEQ ID NO: RRALKKMGD 96) TSCFDEIMKGLKEIKCGGSLEHHHHHH S0_2.58980 MSCEEAKNYIDKQLLPIVNKAGCSRPEELEEMI no (SEQ ID NO: RRALKKMGD 97) TSCFDEIMKGLKEIKCGGSLEHHHHHH S0_2.611 MPCDKQKNYIDKQLLPIVNKAGCGSAKEVQKDI yes (SEQ ID NO: EKALRNAG 98) VKDCLEDILRGIKEWKCGGSLEHHHHHH S0_2.126 MPCDKQKNYIDKQLLPIVNKAGCSRPEEVEERI yes (SEQ ID NO: RRALKKMGD 99) TSCFDEILKGLKEIKCGGSLEHHHHHH
TABLE-US-00007 TABLE 7 Refinement statistics of the S0_2.126 NMR structure. NMR restraints Total NOEs from Unio (a) 306 Intraresidual 124 Interresidual 182 Sequential (i − j = 1) 112 Medium-range (1 < i − j < 5) 47 Long-range (i − j ≥ 5) 23 Dihedral Angles from Talos-n (b) 88 Φ 43 Ψ 45 Structural statistics Violations (c) Distance restraints (Å) 0.0254 ± 0.009 Dihedral angle constraints (°) 6.8 ± 0.12 Ramachandran plot (all residues/ ordered residues)(d) Most favored (%) 84.7/95.8 Additionally allowed (%) 14.3/4.5 Generously allowed (%) 0.98/0.1 Disallowed (%) 0/0 Average pairwise RMSD (Å) (e) Heavy 3.3/1.8 Backbone 2.8/1.2 Structure Quality Factors (raw score/z- Procheck G-factor (phi/psi) 0.15/0.9 Procheck G-factor (all) −0.48/−2.84 (a) From UNIO-ATNOS/CANDID's last cycle (cycle 7) (b) Obtained from chemical shifts with Talos-N server (c) From Cyana in Unio's last cycle (d)All residues from Cyana un Unio's last cycle; ordered residues (5-22, 26-57) from the Protein Structure Validation Suite at http://psvs-1_5-dev.nesg.org/results/testbc/OUTPUT.html (e) From the Protein Structure Validation Suite
indicates data missing or illegible when filed
TABLE-US-00008 TABLE 8 X-ray data collection and refinement statistics of S0_2.126 crystal structure. D25 S0_2.126 Wavelength 0.9763 Resolution range 49.09-3.0 (3.107- Space group P 21 21 21 Unit cell 126.3 127.0 156.1 90 90 90 Total reflections 700184 (72248) Unique reflections 50740 (5000) Multiplicity 13.8 (14.4) Completeness (%) 98.76 (99.22) Mean I/sigma(I) 12.63 (2.00) Wilson B-factor 74.78 R-merge 0.1622 (1.484) R-meas 0.1684 (1.538) R-pim 0.04506 (0.4019) CC1/2 0.999 (0.893) CC* 1 (0.971) Reflections used in
50284 (4971) Reflections used for R-free 2519 (249) R-work 0.2699 (0.3677) R-free 0.2936 (0.3972) CC(work) 0.949 (0.817) CC(free) 0.958 (0.793) Number of non-hydrogen
14453 macromolecules 14452 Protein residues 1921 RMS(bonds) 0.004 RMS(angles) 1.02 Ramachandran favored (%) 94.45 Ramachandran allowed (%) 5.07 Ramachandran outliers (%) 0.48 Rotamer outliers (%) 0.00 Clashscore 7.35 Average B-factor 97.74 macromolecules 97.74 solvent 59.33 Number of TLS groups 12
indicates data missing or illegible when filed
TABLE-US-00009 TABLE 9 X-ray data collection and refinement statistics of S4_2.45 crystal structure. 101F S4_2.45 Wavelength 0.98 Resolution range 38.49-2.6 (2.693- Space group P 32 2 1 Unit cell 148.224 148.224 45.046 Total reflections 113069 (7302) Unique reflections 17464 (1567) Multiplicity 6.5 (4.7) Completeness (%) 98.57 (89.58) Mean I/sigma(I) 17.03 (1.66) Wilson B-factor 56.09 R-merge 0.06712 (0.8361) R-meas 0.07282 (0.9424) R-pim 0.02776 (0.4231) CC1/2 0.999 (0.635) CC* 1 (0.881) Reflections used in
17455 (1565) Reflections used for R-free 1748 (166) R-work 0.2298 (0.3682) R-free 0.2736 (0.3503) CC(work) 0.462 (0.203) CC(free) 0.353 (0.190) Number of non-hydrogen
3794 macromolecules 3686 solvent 108 Protein residues 485 RMS(bonds) 0.010 RMS(angles) 1.46 Ramachandran favored (%) 93.53 Ramachandran allowed (%) 5.64 Ramachandran outliers (%) 0.84 Rotamer outliers (%) 0.96 Clashscore 2.19 Average B-factor 38.90 macromolecules 38.37 solvent 56.78 Number of TLS groups 3
indicates data missing or illegible when filed
REFERENCES FOR METHODS SECTION
[0170] 1. J. S. McLellan et al., Structure of RSV fusion glycoprotein trimer bound to a prefusion-specific neutralizing antibody. Science 340, 1113-1117 (2013). [0171] 2. J. Zhou, G. Grigoryan, Rapid search for tertiary fragments reveals protein sequence-structure relationships. Protein Sci 24, 508-524 (2015). [0172] 3. T. J. Brunette et al., Exploring the repeat protein universe through computational protein design. Nature 528, 580-584 (2015). [0173] 4. J. Bonet et al., Rosetta FunFolDes—A general framework for the computational design of functional proteins. PLoS Comput Biol 14, e1006623 (2018). [0174] 5. X. Hu, H. Wang, H. Ke, B. Kuhlman, High-resolution design of a protein loop. Proc Natl Acad Sci USA 104, 17668-17673 (2007). [0175] 6. P. Conway, M. D. Tyka, F. DiMaio, D. E. Konerding, D. Baker, Relaxation of backbone bond geometry improves protein energy landscape modeling. Protein Sci 23, 47-55 (2014). [0176] 7. J. S. McLellan et al., Structure of a major antigenic site on the respiratory syncytial virus fusion glycoprotein in complex with neutralizing antibody 101F. J Virol 84, 12236-12244 (2010). [0177] 8. V. Mas et al., Engineering, Structure and Immunogenicity of the Human Metapneumovirus F Protein in the Postfusion Conformation. PLoS Pathog 12, e1005859 (2016). [0178] 9. M. D. Tyka et al., Alternate states of proteins revealed by detailed energy landscape mapping. J Mol Biol 405, 607-618 (2011). [0179] 10. M. D. Finucane, M. Tuna, J. H. Lees, D. N. Woolfson, Core-directed protein design. I. An experimental method for selecting stable proteins from combinatorial libraries. Biochemistry 38, 11604-11612 (1999). [0180] 11. P. Kristensen, G. Winter, Proteolytic selection for protein folding using filamentous bacteriophages. Fold Des 3, 321-328 (1998). [0181] 12. A. Chevalier et al., Massively parallel de novo protein design for targeted therapeutics. Nature 550, 74-79 (2017). [0182] 13. F. Sesterhenn et al., Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen. PLoS Biol 17, e3000164 (2019). [0183] 14. G. Chao et al., Isolating and engineering human antibodies using yeast surface display. Nat Protoc 1, 755-768 (2006). [0184] 15. M. G. Joyce et al., Iterative structure-based improvement of a fusion-glycoprotein vaccine against RSV. Nat Struct Mol Biol 23, 811-820 (2016). [0185] 16. B. Briney et al., Tailored Immunogens Direct Affinity Maturation toward HIV Neutralizing Antibodies. Cell 166, 1459-1470 e1411 (2016). [0186] 17. A. Rohou, N. Grigorieff, CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J Struct Biol 192, 216-221 (2015). [0187] 18. J. M. de la Rosa-Trevin et al., Scipion: A software framework toward integration, reproducibility and validation in 3D electron microscopy. J Struct Biol 195, 93-99 (2016). [0188] 19. S. H. Scheres, RELION: implementation of a Bayesian approach to cryo-EM structure determination. J Struct Biol 180, 519-530 (2012). [0189] 20. A. Punjani, J. L. Rubinstein, D. J. Fleet, M. A. Brubaker, cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat Methods 14, 290-296 (2017). [0190] 21. M. Sattler, J. Schleucher, C. Griesinger, Heteronuclear multidimensional NMR experiments for the structure determination of proteins in solution employing pulsed field gradients. Frog Nucl Mag Res Sp 34, 93-158 (1999). [0191] 22. T. Herrmann, P. Guntert, K. Wuthrich, Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS. Journal of Biomolecular Nmr 24, 171-189 (2002). [0192] 23. T. Herrmann, P. Guntert, K. Wuthrich, Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA. Journal of Molecular Biology 319, 209-227 (2002). [0193] 24. D. Gottstein, D. K. Kirchner, P. Guntert, Simultaneous single-structure and bundle representation of protein NMR structures in torsion angle space. J Biomol NMR 52, 351-364 (2012). [0194] 25. Y. Shen, A. Bax, Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. Journal of Biomolecular Nmr 56, 227¬241 (2013). [0195] 26. W. Kabsch, Xds. Acta Crystallogr D 66, 125-132 (2010). [0196] 27. A. J. Mccoy et al., Phaser crystallographic software. J Appl Crystallogr 40, 658-674 (2007). [0197] 28. P. Emsley, B. Lohkamp, W. G. Scott, K. Cowtan, Features and development of Coot. Acta Crystallogr D 66, 486-501 (2010). [0198] 29. P. D. Adams et al., PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr D 66, 213-221 (2010). [0199] 30. P. A. Karplus, K. Diederichs, Linking crystallographic model and data quality. Science 336, 1030-1033 (2012).