Computational redesign of human respiratory syncytial virus epitope as therapeutic peptide vaccines against pediatric pneumonia

Original Paper
  • 74 Downloads

Abstract

Human respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in infants and young children. Here, the RSV fusion (F) glycoprotein epitope FFL was redesigned based on its complex crystal structure with motavizumab, an mAb drug in development for the prevention of RSV infections, aiming to obtain therapeutic peptide vaccines with high affinity to induce RSV-specific neutralizing antibodies. Computational modeling and analysis found that only a small region covering the helix-turn-helix (HTH) motif of FFL can directly interact with motavizumab and confer stability and specificity to the complex system, while the rest of the epitope primarily serves as a structural scaffold that stabilizes the HTH conformation of motavizumab-binding site. Molecular dynamics simulations revealed a large flexibility and intrinsic disorder for the isolated linear HTH peptide, which would incur a considerable entropy penalty upon binding to motavizumab. In this respect, the FFL epitope was redesigned by truncation, mutation, and cyclization to derive a number of small cyclic peptide immunogens. We also employed in vitro fluorescence-based assays to demonstrate that the linear epitope peptide has no observable affinity to motavizumab, whereas redesigned versions of the peptide can bind with a moderate or high potency.

Graphical abstract

Computationally modeled complex structure of RSV F glycoprotein with motavizumab and zoom up of the complex binding site.

Keywords

Respiratory syncytial virus Fusion glycoprotein epitope Rational peptide vaccine design Pediatric pneumonia 

Notes

Compliance with ethical standards

Conflict of interest

The authors report no conflicts of interest.

References

  1. 1.
    Krilov LR (2011) Respiratory syncytial virus disease: update on treatment and prevention. Expert Rev. Anti-Infect. Ther. 9:27–32CrossRefGoogle Scholar
  2. 2.
    Ogra PL (2004) Respiratory syncytial virus: the virus, the disease and the immune response. Paediatr. Respir. Rev. 5:S119–S126CrossRefGoogle Scholar
  3. 3.
    Lanari M, Vandini S, Arcuri S, Galletti S, Faldella G (2013) The use of humanized monoclonal antibodies for the prevention of respiratory syncytial virus infection. Clin Dev Immunol 2013:359683CrossRefGoogle Scholar
  4. 4.
    Wu H, Pfarr DS, Johnson S, Brewah YA, Woods RM, Patel NK, White WI, Young JF, Kiener PA (2007) Development of motavizumab, an ultra-potent antibody for the prevention of respiratory syncytial virus infection in the upper and lower respiratory tract. J. Mol. Biol. 368:652–665CrossRefGoogle Scholar
  5. 5.
    Mejías A, Ramilo O (2008) Review of palivizumab in the prophylaxis of respiratory syncytial virus (RSV) in high-risk infants. Biologics 2:433–439Google Scholar
  6. 6.
    Zhu Q, McAuliffe JM, Patel NK, Palmer-Hill FJ, Yang CF, Liang B, Su L, Zhu W, Wachter L, Wilson S, MacGill RS, Krishnan S, McCarthy MP, Losonsky GA, Suzich JA (2011) Analysis of respiratory syncytial virus preclinical and clinical variants resistant to neutralization by monoclonal antibodies palivizumab and/or motavizumab. J. Infect. Dis. 203:674–682CrossRefGoogle Scholar
  7. 7.
    Correia BE, Bates JT, Loomis RJ, Baneyx G, Carrico C, Jardine JG, Rupert P, Correnti C, Kalyuzhniy O, Vittal V, Connell MJ, Stevens E, Schroeter A, Chen M, Macpherson S, Serra AM, Adachi Y, Holmes MA, Li Y, Klevit RE, Graham BS, Wyatt RT, Baker D, Strong RK, Crowe Jr JE, Johnson PR, Schief WR (2014) Proof of principle for epitope-focused vaccine design. Nature 507:201–206CrossRefGoogle Scholar
  8. 8.
    Ni H, Zhuang Y, Chen Z, Ji W (2017) Molecular engineering of respiratory syncytial virus immunogen against pediatric viral pneumonia. Mol. Simul. 44:1–6Google Scholar
  9. 9.
    McLellan JS, Chen M, Kim A, Yang Y, Graham BS, Kwong PD (2010) Structural basis of respiratory syncytial virus neutralization by motavizumab. Nat. Struct. Mol. Biol. 17:248–250CrossRefGoogle Scholar
  10. 10.
    Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J. Mol. Biol. 285:1735–1747CrossRefGoogle Scholar
  11. 11.
    Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pKa and adding missing hydrogens to macromolecules. Nucleic Acids Res. 33:W368–W371CrossRefGoogle Scholar
  12. 12.
    Case DA, Cheatham 3rd TE, Darden T, Gohlke H, Luo R, Merz Jr KM, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The amber biomolecular simulation programs. J. Comput. Chem. 26:1668–1688CrossRefGoogle Scholar
  13. 13.
    Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J. Comput. Chem. 24:1999–2012CrossRefGoogle Scholar
  14. 14.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein KL (1983) Comparison of simple potential functions for simulating liquid water. J. Phys. Chem. 79:926–935CrossRefGoogle Scholar
  15. 15.
    Yang C, Zhang S, He P, Wang C, Huang J, Zhou P (2015) Self-binding peptides: folding or binding? J. Chem. Inf. Model. 55:329–342CrossRefGoogle Scholar
  16. 16.
    Yang C, Zhang S, Bai Z, Hou S, Wu D, Huang J, Zhou P (2016) A two-step binding mechanism for the self-binding peptide recognition of target domains. Mol. BioSyst. 12:1201–1213CrossRefGoogle Scholar
  17. 17.
    Ryckaert JP, Ciccotti G, Berendsen HJC (1997) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23:327–341CrossRefGoogle Scholar
  18. 18.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald and n.Log(N) method for Ewald sums in large systems. J. Chem. Phys. 98:10089–10092CrossRefGoogle Scholar
  19. 19.
    Zhou S, Wang Q, Ren M, Zhang A, Liu H, Yao X (2017) Molecular dynamics simulation on the inhibition mechanism of peptide-based inhibitor of islet amyloid polypeptide (IAPP) to islet amyloid polypeptide (IAPP22-28) oligomers. Chem. Biol. Drug Des. 90:31–39CrossRefGoogle Scholar
  20. 20.
    Davidchack RL, Handel R, Tretyakov MV (2009) Langevin thermostat for rigid body dynamics. J. Chem. Phys. 130:234101CrossRefGoogle Scholar
  21. 21.
    Yang C, Wang C, Zhang S, Huang J, Zhou P (2015) Structural and energetic insights into the intermolecular interaction among human leukocyte antigens, clinical hypersensitive drugs and antigenic peptides. Mol. Simul. 41:741–751CrossRefGoogle Scholar
  22. 22.
    Zhou P, Zhang S, Wang Y, Yang C, Huang J (2016) Structural modeling of HLA-B*1502/peptide/carbamazepine/T-cell receptor complex architecture: implication for the molecular mechanism of carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis. J. Biomol. Struct. Dyn. 34:1806–1817CrossRefGoogle Scholar
  23. 23.
    Homeyer N, Gohlke H (2012) Free energy calculations by the molecular mechanics Poisson–Boltzmann surface area method. Mol Inf 31:114–122CrossRefGoogle Scholar
  24. 24.
    Tian F, Lv Y, Zhou P, Yang L (2011) Characterization of PDZ domain–peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses. J. Comput. Aided Mol. Des. 25:947–958CrossRefGoogle Scholar
  25. 25.
    Bahar I, Lezon TR, Bakan A, Shrivastava IH (2010) Normal mode analysis of biomolecular structures: functional mechanisms of membrane proteins. Chem. Rev. 110:1463–1497CrossRefGoogle Scholar
  26. 26.
    Zhang X, Pickin KA, Bose R, Jura N, Cole PA, Kuriyan J (2007) Inhibition of the EGF receptor by binding of MIG6 to an activating kinase domain interface. Nature 450:741–744CrossRefGoogle Scholar
  27. 27.
    Kaustov L, Ouyang H, Amaya M, Lemak A, Nady N, Duan S, Wasney GA, Li Z, Vedadi M, Schapira M, Min J, Arrowsmith CH (2011) Recognition and specificity determinants of the human cbx chromodomains. J. Biol. Chem. 286:521–529CrossRefGoogle Scholar
  28. 28.
    Schickli JH, Whitacre DC, Tang RS, Kaur J, Lawlor H, Peters CJ, Jones JE, Peterson DL, McCarthy MP, Van Nest G, Milich DR (2015) Palivizumab epitope-displaying virus-like particles protect rodents from RSV challenge. J. Clin. Invest. 125:1637–1647CrossRefGoogle Scholar
  29. 29.
    Zhou P, Tian F, Shang Z (2009) 2D depiction of nonbonding interactions for protein complexes. J. Comput. Chem. 30:940–951CrossRefGoogle Scholar
  30. 30.
    London N, Raveh B, Cohen E, Fathi G, Schueler-Furman O (2011) Rosetta FlexPepDock web server—high resolution modeling of peptide-protein interactions. Nucleic Acids Res. 39:W249–W253CrossRefGoogle Scholar
  31. 31.
    Luo H, Du T, Zhou P, Yang L, Mei H, Ng H, Zhang W, Shu M, Tong W, Shi L, Mendrick DL, Hong H (2015) Molecular docking to identify associations between drugs and class I human leukocyte antigens for predicting idiosyncratic drug reactions. Comb. Chem. High Throughput Screen. 18:296–304CrossRefGoogle Scholar
  32. 32.
    Yu H, Zhou P, Deng M, Shang Z (2014) Indirect readout in protein-peptide recognition: a different story from classical biomolecular recognition. J. Chem. Inf. Model. 54:2022–2032CrossRefGoogle Scholar
  33. 33.
    Patgiri A, Jochim AL, Arora PS (2008) A hydrogen bond surrogate approach for stabilization of short peptide sequences in alpha-helical conformation. Acc. Chem. Res. 41:1289–1300CrossRefGoogle Scholar
  34. 34.
    Walensky LD, Bird GH (2014) Hydrocarbon-stapled peptides: principles, practice, and progress. J. Med. Chem. 57:6275–6288CrossRefGoogle Scholar
  35. 35.
    Bai Z, Hou S, Zhang S, Li Z, Zhou P (2017) Targeting self-binding peptides as a novel strategy to regulate protein activity and function: a case study on the proto-oncogene tyrosine protein kinase c-Src. J. Chem. Inf. Model. 57:835–845CrossRefGoogle Scholar
  36. 36.
    Zhou P, Hou S, Bai Z, Li Z, Wang H, Chen Z, Meng Y (2017) Disrupting the intramolecular interaction between proto-oncogene c-Src SH3 domain and its self-binding peptide PPII with rationally designed peptide ligands. Artif Cells Nanomed Biotechnol.  https://doi.org/10.1080/21691401.2017.1360327
  37. 37.
    Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng. 8:127–134CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PediatricsHuai’an Affiliated Hospital of Xuzhou Medical University, The Second People’s Hospital of Huai’anHuai’anPeople’s Republic of China
  2. 2.Department of PediatricsCentral Hospital of Shanghai Minhang DistrictShanghaiPeople’s Republic of China

Personalised recommendations