Skip to main content
NCBI home page
ACS Medicinal Chemistry Letters logoLink to ACS Medicinal Chemistry Letters
. 2026 Sep 21;17(10):2191–2196. doi: 10.1021/acsmedchemlett.6c00321

Antibody Hotspot-Centric de Novo Design of Peptide Inhibitors of Influenza A Virus Membrane Fusion

Rameshwar U Kadam †,*, Jarek Juraszek ‡, Davide Branduardi ‡, Jeroen van Ameijde ‡, Divita Garg †, Jan Vermond ‡, Mandy Jongeneelen ‡, Chan Tang ‡, Ronald Vogels ‡, Maria J P van Dongen ‡, Boerries Brandenburg ‡, Robert H E Friesen ‡, Ian A Wilson †,§,*
PMCID: PMC13656271  PMID: 42866404

Abstract

Here we report the applicability of a hotspot-centric approach for de novo computational design of l-peptides based on crystal structures of antigen–antibody complexes. Broadly neutralizing antibodies (bnAbs) FI6v3 and CR9114, which target influenza A virus hemagglutinin (HA), guided the peptide design. Disembodied hotspot residues from these bnAbs were selected and anchored onto known peptide scaffolds (≤35 amino acids) from the Protein Data Bank (PDB). The top-scoring designs were subsequently synthesized, folded, tested in vitro, and structurally characterized in complex with HA. The designed peptides demonstrate structural and functional mimicry by recapitulating the binding mode of the bnAbs on HA and act as inhibitors of low-pH-dependent conformational transitions in HA that facilitate membrane fusion. These findings demonstrate that hotspot-guided design strategy, previously applied to proteins and d-peptides, can be effectively extended to l-peptide scaffolds, enabling the development of functional HA inhibitors informed by key residues at antigen–antibody interfaces.

Keywords: Antibody hotspots, de novo design, influenza hemagglutinin, peptides, fusion inhibitors

graphic file with name ml6c00321_0005.webp

graphic file with name ml6c00321_0004.webp

A structural and mechanistic understanding of protein–protein interactions (PPIs) provides pivotal insights for the development of novel therapeutic modalities. − Various strategies such as in vitro display, , de novo design, molecular grafting, structure-based rational design, − and most recently artificial intelligence (AI)-based deep learning and generative approaches, − have been employed to mimic key structural elements or recapitulating energetically favorable hotspot residues at the protein–protein interfaces. Antibody hotspot-centric de novo design is one such approach that has been successfully applied in the design of small proteins that bind to influenza A hemagglutinin (HA). − In this approach, the hotspot residues from antibodies in complex with HA were grafted onto stable and well-folded protein scaffolds from the PDB and computationally and experimentally evaluated to design protein-based potent HA binders. The success of transplanting antibody interactions onto protein scaffolds encouraged us to further explore the design of much smaller l-peptide ligands through an analogous approach (Figure ).

1.

1

Open in a new tab

Schematic illustration of antibody hotspot-centric peptide design approach. (A) Crystal structure of influenza A hemagglutinin (HA) in complex with antibody CR9114 Fab (PDB 4FQI). The HA trimer with CR9114 is represented as a backbone cartoon with transparent molecular surface with one protomer colored (HA1, wheat; HA2, cyan; and CR9114 Fab-green) and the other two protomers in gray. CR9114 Fab hotspots residues represented as (green sticks with surface). (B) Selected peptide scaffolds from the PDB (gray cartoons). (C) Antibody hotspot-centric designed peptides.

Fleishman et al. previously developed an antibody hotspot-centric design framework for generating small protein binders to the influenza HA stem. More recently, we showed that this strategy can also be applied to flexible, readily synthesized peptides shorter than 35 amino acids through the design of d-peptide ligands. Leveraging this strategy, the present study further extends the approach to l-peptide ligands targeting HA. l-Peptide ligands combine high target specificity with efficient, scalable synthesis, offering manufacturing and cost advantages over complex protein-based therapeutics, while avoiding the synthetic challenges associated with producing fully d-configured peptides. − Our design methodology employs nuclear magnetic resonance (NMR)-derived peptide structures from the PDB and incorporates noncanonical amino acids to optimize interaction geometry and stability.

To design the peptides, we used co-crystal structures of broadly neutralizing anti-influenza antibodies in complex with HA, specifically the CR9114 fragment antigen-binding (Fab) bound to H5N1 A/Vietnam/1203/2004 HA (H5/Viet; PDB 4FQI) and the FI6v3 Fab bound to H1N1 A/California/04/2009 HA (H1/Cal; PDB 3ZTN). For each complex, hotspot residues were defined as Fab amino acids containing more than 80% of their side-chain heavy atoms (including Cα) within 4 Å of the HA epitope and comprising at least five such atoms. Residues that could not be independently redocked using Rosetta were excluded. Based on these criteria, Phe54, Phe74, and Tyr98 from CR9114 and Phe100D and Trp100F from FI6v3 were selected (nb: for clarity, the two CR9114 hotspot residues in the designs are shown as the representative example in Figure ). Phe74 was not retained in any design that survived the subsequent redesign and filtering steps and consequently does not appear in the final library (Table ).

1. Hotspot-Centric de Novo Designed Peptides with Their Affinity and Specificity Towards Influenza A HA (H1/Cal).

peptide IDs PDB bnAbs hotspot residues peptide sequences K i CH65 (μM) K i HB80.4 (μM) K i CH65/K i HB80.4
DP141980 1ACW CR9114 Y98 H-&VSCEDCPEHCSTQKATAYCYNDKCVCQPI-OH >236 33 >7.2
DP141983 1C49 NT NT H-&TISCTNEKQCYPHCKKETGYPNAKCMNRKCKCFGR-OH >236 31 >7.6
DP141985 1CMR FI6v3 F100D, W100F H-CTSSKECWSVCQRLHNTSKGFCSHRGCRCWS&-NH2 >236 50 >4.7
DP141993 1FSV CR9114 F54 H-&QQYTAKVKGRTFRNSREAFDFIEKFKGR-NH2 >236 >58 ND
DP141995 1HP3 CR9114 F54, Y98 H-LQRCLFGNGRCSSHQDCCYLTPVCFRGSCVSS&-NH2 >236 >58 ND
DP141999 1KIO CR9114 F54 H-&EVTCEPGTTHFDKCDICRCGSDGKSAACHNMACPQ-OH >236 >58 ND
DP142001 1LA4 CR9114 F54 H-&TCRYLFGGCKHISDCCKHLFCSEDGKYCRWNGTF-OH 209 51 4.1
DP142006 1PJV FI6v3 F100D H-&AVCVSRTCJKDCQRRGYASGFCFNNACKCSPY-OH >236 >58 ND
DP142010 1QUZ FI6v3 F100D H-&ASCRRPEDCMDPCRKETGCPSGLCFNRKCKCNRC-NH2 230 21 10.7
DP142014 1RIK CR9114 F54, Y98 H-&YKYACPQCPKRFMRFDHLXLHXLQHFKKK-OH >236 16 >14.9
DP142020 1SCY CR9114 F54 H-&AFCNLRMCQLSCRSLGKLGQCFGNRCXCVNH-NH2 >236 51 >4.7
DP142028 1WQD NT NT H-DPCYEVCLQQHGNVKECEEACK&-NH2 >236 54 >4.4
DP142032 1ZJQ FI6v3 F100D H-GCGGAMAGCDGKSTFCCSGFNCSPTAKACFRARP&-NH2 207 22 9.4
DP142035 2EEM CR9114 F54, Y98 H-SCFIRCYGHCRARRCGYSVAVLYRGRCYCKCLRC&-NH2 >236 29 >8.1
DP142038 2KNP NT NT H-&GCEGKQCGLFRSCGGGCRCWPTVTPGVGICSSS-NH2 >236 >58 ND
DP142044 2PO8 NT NT H-SPKILKKCRRDSDCPGASICRGNGYCGSGSDGGV&-NH2 >236 >58 ND
Open in a new tab
a

Competitive inhibition constant (K i ) values of peptides were determined against influenza H1/Cal HA in competition with HA head binding antibody Fab CH65 and stem targeting small protein HB80.4 using a competition ELISA. Affinity values are reported in micromolar (μM).

b

Specificity values were defined as the ratio K i CH65/K i HB80.4. Noncanonical amino acids are ‘&’ (l-propargylglycine); ‘X’ (l-norleucine), and ‘J’ (l-aminobutyric acid). ND: Not Determined.

c

Hotspot residues from HA stem-targeted antibodies. NT: Non transplanted peptides (bnAb FI6v3 and CR9114 hotspot residues were not used in the design of these peptides)

Next, using the hotspot residues identified from the antibody–HA complexes, we computationally screened a pre-selected set of 315 peptide scaffolds derived from structures in the PDB (Table S1 and Figure S1). Most of these scaffolds were determined by NMR and are deposited as multiple conformers. Each conformer was treated as an independent rigid docking input, giving 6,749 inputs in total, and all were docked onto HA to identify candidates capable of accommodating the hotspot geometries while complementing the HA surface. To improve the likelihood of obtaining suitable matches, the hotspot residues were excised from the Fab together with their backbone atoms, superimposed onto the target HA, and redocked using RosettaDock with inverse-rotamer generation. This process produced hundreds of alternative hotspot placements (stub sets) in which the side chain retains its original interaction with HA while the backbone adopts a different position and orientation. A candidate scaffold backbone needs to superimpose on only one member of the set to constitute a match. ,,

Scaffolds that could be docked onto HA while recapitulating any pairwise combination of the hotspot residues identified for a given bnAb were designated as hits. Because hits were designed with any pairwise combination of hotspots, and because scaffold backbone geometry determines which pair can be accommodated, individual designs differ in the hotspot residues that they display (Table ). During the subsequent iterative redesign, a subset of designs adopted a slightly shifted docking orientation in which one hotspot side chain no longer occupied its original subsite. Designs where the overall docked pose remained favorable through shape complementarity and additional interactions introduced during redesign were retained. These hit scaffolds were then subjected to iterative refinement using RosettaDesign and RosettaDock to generate focused peptide libraries.

During this redesign stage, peptide sequences were systematically mutated with canonical and noncanonical amino acids to optimize both the Rosetta score, a proxy for binding free energy, and a binding-efficiency metric defined as binding free energy per introduced mutation. This metric was incorporated to enrich for hotspot-like interactions while favoring compact scaffolds requiring minimal sequence alteration, as each mutation in a short peptide increases the risk of misfolding. Because the design protocol was performed on a set of conformations for each peptide family, favorable substitutions that emerged independently at different positions within a scaffold family were, in some cases, not sampled in combination. A small number of such composite variants were therefore constructed manually at the end of the pipeline and evaluated under the same scoring, redocking and acceptance criteria as the automated designs [see also Supporting Information (SI)]. Designs in which the hotspot amino acids had mutated away were removed. Depending on the scaffold and the number of mutations introduced, 20–80% of the peptides within each scaffold class were nominated for chemical synthesis, followed by the in vitro folding, and binding experiments.

The final library consisted of 16 peptides spanning multiple scaffold classes, including four designs in which no hotspot residue was transplanted (Table ; Figure S2 and S3; Table S2). In these cases, the scaffold already presented a solvent-exposed aromatic residue at the position selected by the matching algorithm, so the hotspot was satisfied by the native residue and no substitution was introduced; the sequences of these four peptides are therefore identical to those of the parent PDB scaffolds (Table S2). Binding activity was evaluated using a competition ELISA against HA from the 2009 pandemic H1N1 A/California/07/2009 strain (H1/Cal). In competition assays using HA head (Fab CH65) and stem binder (designed small protein HB80.4), the peptides showed clear competition for the stem region, exhibiting competitive inhibition constants (K i ) of 16–51 μM and specificities of 7–14, defined as the ratio K i CH65/K i HB80.4 (Table ). Two peptides, DP142010 and DP142014 derived from the hotspot residues of bnAbs FI6v3 and CR9114, respectively, displayed specificities greater than 10. Among these, DP142014 demonstrated the highest affinity and specificity toward H1/Cal HA (Table ).

To validate the antibody hotspot-centric de novo design peptide, we determined a co-crystal structure of peptide DP142014 in complex with HA from H1N1 A/Puerto Rico/8/1934 (H1/PR8) at 2.85 Å resolution (Figure ; Figure S4; Table S3). The PR8 HA strain was used for crystallization because of its robust crystallization properties and extensive structural characterization across multiple binding modalities. , Furthermore, conservation of the HA stem epitope and peptide-contacting residues between PR8 and A/South Carolina/1/1918 supports its use as an appropriate crystallographic surrogate. DP142014 engages a largely hydrophobic surface containing small non-polar cavities at the HA1–HA2 interface within the HA stem region (Figure A–C). This site is highly conserved across influenza A group 1 and group 2 HAs, as well as influenza B viruses, and is the target of HA-stem broadly neutralizing antibodies. , The co-crystal structure shows that DP142014 closely mimics the computational design model by occupying the same hydrophobic groove at the HA1/HA2 interface (Figure C–G), with only minor differences in local details. Importantly, the binding mode of the CR9114-derived hotspot residues (Phe54 and Tyr98) is faithfully recapitulated (Figure G). As expected for flexible termini, several solvent-exposed residues at the N- and C-termini of DP142014 are disordered in the crystal structure (Figure C,E). Molecular interaction analysis revealed that DP142014 engages HA through a combination of nonpolar and polar interactions. Peptide residues Tyr 3 , Phe 15 , X19 , Leu 20 , and Leu 23 form extensive nonpolar contacts with His18, His38, Ser291, and Thr318 on HA1, as well as Trp21, Thr41, Ile48, Val55 on HA2, thereby establishing predominantly hydrophobic interactions (Figure B,C). In addition, the side-chain hydroxyl of Tyr 3 and the side-chain carboxylate of Asp 16 forms hydrogen bonds with the backbone carbonyl groups of Asp19 and Ile18 on HA2, respectively (Figure H). The backbone amide and carbonyl of Ala 4 also participate in hydrogen bonding with Gln42 from helix A of HA2 (Figure H). DP142014 buries ∼148 Å2 on HA1 and ∼488 Å2 on HA2, which are values comparable to those buried by the CR9114 Fab (∼276 Å2 on HA1 and ∼511 Å2 on HA2) (Figure H). Together, these interactions demonstrate that the designed peptide DP142014 faithfully emulates the binding mode of HA-stem-targeting antibodies and recapitulates their key molecular contacts.

2.

2

Open in a new tab

Crystal structure of peptide DP142014 in complex with influenza group-1 H1 HA from H1N1 A/Puerto Rico/8/1934 (H1/PR8) strain. A protomer of the HA trimer is represented as a backbone cartoon with its molecular surface (HA1; wheat and HA2; cyan). DP142014 is shown in ribbon representation (purple). (B,C) A zoomed in view of DP142014 binding sites in the HA protomer is shown with the C/O/N/S atoms of DP142014 in purple, red, blue, and yellow, respectively. The dotted pink ribbon indicates disordered residues in DP142014 in the crystal structure. (D) 2D-representation of DP142014. (E) 2Fo-Fc map (blue color mesh) contoured at 1σ is displayed around the bound conformation of DP142014 in complex with H1/PR8 HA. (F) Computational model of the de novo designed structure of DP142014 (cartoon with yellow sticks) in complex with HA. (G) Comparison of position of CR9114 Fab hotspot residues (green) with residues from the model (yellow) and X-ray structure (pink) of DP142014 in complex with the HA. (H) Molecular interactions of DP142014 with H1/PR8. Hydrogen bonds are depicted in black dotted lines and measured in Å. Buried surface area (BSA, Å2) on (HA1/HA2) by CR9114 Fab and DP142014. Single-letter abbreviations for the amino acid residues are as follows: A, Ala; C, Cys; D, Asp; E, Glu; F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; and Y, Tyr.

Since the structural analysis of the DP142014-HA complex indicated that the peptide mimics the binding mode of stem-targeting bnAbs, we next examined whether it also recapitulates their mechanism of action. As in prior studies of protein-based, ,, peptide-based, , and small-molecule HA fusion inhibitors, we employed a trypsin susceptibility (TS) assay to assess the ability of inhibitors to block the low-pH-induced conformational rearrangements of HA.

Consistent with the behavior of Fab CR9114, which prevents HA from undergoing its low-pH conformational transition (Figure , lane 9), DP142014 similarly inhibited conversion of H1/PR8 HA to its postfusion state, as evidenced by its protection of HA from trypsin digestion at pH 5.0 (Figure , lane 11).

3.

3

Open in a new tab

Mechanism of HA inhibition by DP142014. The trypsin susceptibility assay establishes that DP142014 inhibits the low pH-induced conformational changes in H1/PR8 HA. Exposure to low pH renders the H1/PR8 HA sensitive to trypsin digestion (lanes 7 vs 8). By similar mechanism to that of fusion inhibiting HA stem targeting CR9114 Fab (lanes 9 vs 10), peptide DP142014 prevents its conversion to a trypsin-susceptible conformation (lanes 11 vs 12).

In conclusion, we successfully applied an antibody hotspot-centric approach to design and screen small, flexible l-peptide ligands targeting influenza A hemagglutinin. From a 16-member library, this strategy yielded two promising hits (DP142010 and DP142014), including one peptide that was co-crystallized with HA and shown to recapitulate both the structural and functional features of stem-targeting bnAbs. These findings underscore the efficiency and cost-effectiveness of peptide-based screening and highlight the potential of compact peptides to emulate key antibody interactions. With continued advances in computational design and AI-driven methodologies, we anticipate further improvements in both the speed and success rate of designing small peptide ligands. Collectively, this work establishes antibody hotspot-centric design as a robust and scalable approach for developing next-generation peptide therapeutics.

Supplementary Material

ml6c00321_si_001.pdf (2.6MB, pdf)

Acknowledgments

The X-ray data set was collected at the Advanced Photon Source, Argonne National Laboratory (beamline 23 ID-D). GM/CA CAT is funded in whole or in part with federal funds from the National Cancer Institute (Y1-CO-1020) and NIGMS (Y1-GM-1104). Use of the Advanced Photon Source was supported by the U.S. Department of Energy (DOE), Basic Energy Sciences, Office of Science, under contract no. DE-AC02-06CH11357. All data and code to understand and assess the conclusions of this research are available in the main text, supplementary materials and via the following repositories: Protein Data Bank accession code https://www.rcsb.org/structure/12AO.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsmedchemlett.6c00321.

  • Computational and experimental materials and methods (including peptide design, in vitro folding of peptides, HPLC and LC-MS spectra, competition ELISA assay, expression and purification of HA, X-ray data collection and refinement statistics) (PDF)

⊥.

Structural & Protein Sciences, Johnson and Johnson Innovative Medicine, 3210 Merryfield Row, San Diego, CA, 92121, United States. orcid.org/0000-0002-1612-9250

#.

Avidicure, 2342 DH, Oegstgeest, The Netherlands

∇.

Astex Pharmaceuticals, Cambridge, CB4 0QA, United Kingdom

○.

Scenic Biotech, 1098 XH Amsterdam, The Netherlands

◆.

Allegria Therapeutics, Aeschenvorstadt 48, 4501 Basel, Switzerland.

∥.

R.U.K and J.J. contributed equally.

This work was supported in part by NIH grants R56 AI117675 and R56 AI127371 (to I.A.W.).

The authors declare the following competing financial interest(s): During the study, J.J, D.B., J.A., J.V., M.J., C.T., R.V., M.J.P.v.D., B.B., and R.H.E.F. were employees at Johnson & Johnson and may hold equity in the company.

Safety Statement. No unexpected or unusually high safety hazards were encountered.

References

  1. Arkin M. R., Tang Y., Wells J. A.. Small-molecule inhibitors of protein-protein interactions: progressing toward the reality. Chem. Biol. 2014;21:1102–14. doi: 10.1016/j.chembiol.2014.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Laraia L., McKenzie G., Spring D. R., Venkitaraman A. R., Huggins D. J.. Overcoming chemical, biological, and computational challenges in the development of inhibitors targeting protein-protein interactions. Chem. Biol. 2015;22:689–703. doi: 10.1016/j.chembiol.2015.04.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Zinzalla G., Thurston D. E.. Targeting protein-protein interactions for therapeutic intervention: a challenge for the future. Future Med. Chem. 2009;1:65–93. doi: 10.4155/fmc.09.12. [DOI] [PubMed] [Google Scholar]
  4. Scott D. E., Bayly A. R., Abell C., Skidmore J.. Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat. Rev. Drug Discov. 2016;15:533–50. doi: 10.1038/nrd.2016.29. [DOI] [PubMed] [Google Scholar]
  5. Wells J. A., McClendon C. L.. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature. 2007;450:1001–9. doi: 10.1038/nature06526. [DOI] [PubMed] [Google Scholar]
  6. Hong S. H., Nguyen T., Ongkingco J. F., Nazzaro A., Arora P. S.. From Concepts to Inhibitors: A blueprint for targeting protein-protein interactions. Chem. Rev. 2025;125:6819–6869. doi: 10.1021/acs.chemrev.5c00046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Nada H., Choi Y., Kim S., Jeong K. S., Meanwell N. A., Lee K.. New insights into protein-protein interaction modulators in drug discovery and therapeutic advance. Signal Transduct Target Ther. 2024;9:341. doi: 10.1038/s41392-024-02036-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bakhshinejad B., Kjaer A.. NGS and the design of an optimized phage display workflow for peptide discovery. Amino Acids. 2026;58:5. doi: 10.1007/s00726-025-03492-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hutchings C. J., Sato A. K.. Phage display technology and its impact in the discovery of novel protein-based drugs. Expert Opin Drug Discov. 2024;19:887–915. doi: 10.1080/17460441.2024.2367023. [DOI] [PubMed] [Google Scholar]
  10. Pan X., Kortemme T.. Recent advances in de novo protein design: Principles, methods, and applications. J. Biol. Chem. 2021;296:100558. doi: 10.1016/j.jbc.2021.100558. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Wang C. K., Craik D. J.. Designing macrocyclic disulfide-rich peptides for biotechnological applications. Nat. Chem. Biol. 2018;14:417–427. doi: 10.1038/s41589-018-0039-y. [DOI] [PubMed] [Google Scholar]
  12. Kadam R. U., Juraszek J., Brandenburg B., Buyck C., Schepens W. B. G., Kesteleyn B., Stoops B., Vreeken R. J., Vermond J., Goutier W., Tang C., Vogels R., Friesen R. H. E., Goudsmit J., van Dongen M. J. P., Wilson I. A.. Potent peptidic fusion inhibitors of influenza virus. Science. 2017;358:496–502. doi: 10.1126/science.aan0516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Kadam R. U., Juraszek J., Brandenburg B., Garg D., Zhu X., Jongeneelen M., Schepens W. B. G., Stoops B., Vermond J., Goutier W., Tang C., Blokland S., Vogels R., Friesen R. H. E., van Dongen M. J. P., Wilson I. A.. VHH antibody loop guides design of a synthetic macrocyclic peptide that potently blocks influenza virus membrane fusion. npj Viruses. 2025;3:83. doi: 10.1038/s44298-025-00166-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Harrison R. S., Shepherd N. E., Hoang H. N., Ruiz-Gomez G., Hill T. A., Driver R. W., Desai V. S., Young P. R., Abbenante G., Fairlie D. P.. Downsizing human, bacterial, and viral proteins to short water-stable alpha helices that maintain biological potency. Proc. Natl. Acad. Sci. U. S. A. 2010;107:11686–91. doi: 10.1073/pnas.1002498107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Reid R. C., Yau M. K., Singh R., Hamidon J. K., Reed A. N., Chu P., Suen J. Y., Stoermer M. J., Blakeney J. S., Lim J., Faber J. M., Fairlie D. P.. Downsizing a human inflammatory protein to a small molecule with equal potency and functionality. Nat. Commun. 2013;4:2802. doi: 10.1038/ncomms3802. [DOI] [PubMed] [Google Scholar]
  16. Bennett N. R., Coventry B., Goreshnik I., Huang B., Allen A., Vafeados D., Peng Y. P., Dauparas J., Baek M., Stewart L., DiMaio F., De Munck S., Savvides S. N., Baker D.. Improving de novo protein binder design with deep learning. Nat. Commun. 2023;14:2625. doi: 10.1038/s41467-023-38328-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Anishchenko I., Pellock S. J., Chidyausiku T. M., Ramelot T. A., Ovchinnikov S., Hao J., Bafna K., Norn C., Kang A., Bera A. K., DiMaio F., Carter L., Chow C. M., Montelione G. T., Baker D.. De novo protein design by deep network hallucination. Nature. 2021;600:547–552. doi: 10.1038/s41586-021-04184-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Watson J. L., Juergens D., Bennett N. R., Trippe B. L., Yim J., Eisenach H. E., Ahern W., Borst A. J., Ragotte R. J., Milles L. F., Wicky B. I. M., Hanikel N., Pellock S. J., Courbet A., Sheffler W., Wang J., Venkatesh P., Sappington I., Torres S. V., Lauko A., De Bortoli V., Mathieu E., Ovchinnikov S., Barzilay R., Jaakkola T. S., DiMaio F., Baek M., Baker D.. De novo design of protein structure and function with RFdiffusion. Nature. 2023;620:1089–1100. doi: 10.1038/s41586-023-06415-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ekambaram S., Dokholyan N. V.. Peptide-based drug design using generative AI. Chem. Commun. 2026;62:672–691. doi: 10.1039/D5CC04998A. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Goles M., Daza A., Cabas-Mora G., Sarmiento-Varon L., Sepulveda-Yanez J., Anvari-Kazemabad H., Davari M. D., Uribe-Paredes R., Olivera-Nappa A., Navarrete M. A., Medina-Ortiz D.. Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides. Brief. Bioinform. 2024;25:bbae275. doi: 10.1093/bib/bbae275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fleishman S. J., Corn J. E., Strauch E. M., Whitehead T. A., Karanicolas J., Baker D.. Hotspot-centric de novo design of protein binders. J. Mol. Biol. 2011;413:1047–62. doi: 10.1016/j.jmb.2011.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Whitehead T. A., Chevalier A., Song Y., Dreyfus C., Fleishman S. J., De Mattos C., Myers C. A., Kamisetty H., Blair P., Wilson I. A., Baker D.. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat. Biotechnol. 2012;30:543–8. doi: 10.1038/nbt.2214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fleishman S. J., Whitehead T. A., Ekiert D. C., Dreyfus C., Corn J. E., Strauch E. M., Wilson I. A., Baker D.. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science. 2011;332:816–21. doi: 10.1126/science.1202617. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Juraszek J., Kadam R. U., Branduardi D., van Ameijde J., Garg D., Dailly N., Jongeneelen M., Vermond J., Brakenhoff J. P. J., Brandenburg B., van Dongen M. J. P., Vogels R., Friesen R. H. E., Wilson I. A.. De novo design of D-peptide ligands: Application to influenza virus hemagglutinin. Proc. Natl. Acad. Sci. U. S. A. 2025;122:e2426554122. doi: 10.1073/pnas.2426554122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Wang L., Wang N., Zhang W., Cheng X., Yan Z., Shao G., Wang X., Wang R., Fu C.. Therapeutic peptides: current applications and future directions. Signal Transduct Target Ther. 2022;7:48. doi: 10.1038/s41392-022-00904-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Craik D. J., Fairlie D. P., Liras S., Price D.. The future of peptide-based drugs. Chem. Biol. Drug Des. 2013;81:136–47. doi: 10.1111/cbdd.12055. [DOI] [PubMed] [Google Scholar]
  27. Gare C. L., White A. M., Malins L. R.. From lead to market: chemical approaches to transform peptides into therapeutics. Trends Biochem Sci. 2025;50:467–480. doi: 10.1016/j.tibs.2025.01.009. [DOI] [PubMed] [Google Scholar]
  28. Lander A. J., Jin Y., Luk L. Y. P.. D-Peptide and D-protein technology: recent advances, challenges, and opportunities. Chembiochem. 2023;24:e202200537. doi: 10.1002/cbic.202200537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Xiao W., Jiang W., Chen Z., Huang Y., Mao J., Zheng W., Hu Y., Shi J.. Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines. Signal Transduct Target Ther. 2025;10:74. doi: 10.1038/s41392-024-02107-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lombardi L., Genio V. D., Albericio F., Williams D. R.. Advances in peptidomimetics for next-generation therapeutics: strategies, modifications, and applications. Chem. Rev. 2025;125:7099–7166. doi: 10.1021/acs.chemrev.4c00989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Dreyfus C., Laursen N. S., Kwaks T., Zuijdgeest D., Khayat R., Ekiert D. C., Lee J. H., Metlagel Z., Bujny M. V., Jongeneelen M., van der Vlugt R., Lamrani M., Korse H. J., Geelen E., Sahin O., Sieuwerts M., Brakenhoff J. P., Vogels R., Li O. T., Poon L. L., Peiris M., Koudstaal W., Ward A. B., Wilson I. A., Goudsmit J., Friesen R. H.. Highly conserved protective epitopes on influenza B viruses. Science. 2012;337:1343–8. doi: 10.1126/science.1222908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Corti D., Voss J., Gamblin S. J., Codoni G., Macagno A., Jarrossay D., Vachieri S. G., Pinna D., Minola A., Vanzetta F., Silacci C., Fernandez-Rodriguez B. M., Agatic G., Bianchi S., Giacchetto-Sasselli I., Calder L., Sallusto F., Collins P., Haire L. F., Temperton N., Langedijk J. P., Skehel J. J., Lanzavecchia A.. A neutralizing antibody selected from plasma cells that binds to group 1 and group 2 influenza A hemagglutinins. Science. 2011;333:850–6. doi: 10.1126/science.1205669. [DOI] [PubMed] [Google Scholar]
  33. Whittle J. R., Zhang R., Khurana S., King L. R., Manischewitz J., Golding H., Dormitzer P. R., Haynes B. F., Walter E. B., Moody M. A., Kepler T. B., Liao H. X., Harrison S. C.. Broadly neutralizing human antibody that recognizes the receptor-binding pocket of influenza virus hemagglutinin. Proc. Natl. Acad. Sci. U. S. A. 2011;108:14216–21. doi: 10.1073/pnas.1111497108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kuhlman B., Dantas G., Ireton G. C., Varani G., Stoddard B. L., Baker D.. Design of a novel globular protein fold with atomic-level accuracy. Science. 2003;302:1364–8. doi: 10.1126/science.1089427. [DOI] [PubMed] [Google Scholar]
  35. Gray J. J., Moughon S., Wang C., Schueler-Furman O., Kuhlman B., Rohl C. A., Baker D.. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J. Mol. Biol. 2003;331:281–99. doi: 10.1016/S0022-2836(03)00670-3. [DOI] [PubMed] [Google Scholar]
  36. Krause J. C., Ekiert D. C., Tumpey T. M., Smith P. B., Wilson I. A., Crowe J. E. Jr.. An insertion mutation that distorts antibody binding site architecture enhances function of a human antibody. mBio. 2011;2:e00345-10. doi: 10.1128/mBio.00345-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Koday M. T., Nelson J., Chevalier A., Koday M., Kalinoski H., Stewart L., Carter L., Nieusma T., Lee P. S., Ward A. B., Wilson I. A., Dagley A., Smee D. F., Baker D., Fuller D. H.. A Computationally designed hemagglutinin stem-binding protein provides in vivo protection from influenza independent of a host immune response. PLoS Pathog. 2016;12:e1005409. doi: 10.1371/journal.ppat.1005409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kadam R. U., Juraszek J., Brandenburg B., Garg D., Zhu X., Jongeneelen M., Tang C., Schepens W. B. G., Buyck C., Stoops B., Vermond J., Vogels R., Friesen R. H. E., van Dongen M. J. P., Wilson I. A.. Small molecule-constrained paratope mimetic bicyclic peptides as potent inhibitors of group 1 and 2 influenza A virus hemagglutinins. Proc. Natl. Acad. Sci. U. S. A. 2026;123:e2537533123. doi: 10.1073/pnas.2537533123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. van Dongen M. J. P., Kadam R. U., Juraszek J., Lawson E., Brandenburg B., Schmitz F., Schepens W. B. G., Stoops B., van Diepen H. A., Jongeneelen M., Tang C., Vermond J., van Eijgen-Obregoso Real A., Blokland S., Garg D., Yu W., Goutier W., Lanckacker E., Klap J. M., Peeters D. C. G., Wu J., Buyck C., Jonckers T. H. M., Roymans D., Roevens P., Vogels R., Koudstaal W., Friesen R. H. E., Raboisson P., Dhanak D., Goudsmit J., Wilson I. A.. A small-molecule fusion inhibitor of influenza virus is orally active in mice. Science. 2019;363:eaar6221. doi: 10.1126/science.aar6221. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ml6c00321_si_001.pdf (2.6MB, pdf)

Articles from ACS Medicinal Chemistry Letters are provided here courtesy of American Chemical Society

RESOURCES