Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

Nature Biotechnology volume 30, pages 543–548 (2012)Cite this article

Subjects

Abstract

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Sequence-function landscapes of designed influenza-binding proteins.
Figure 2: Improvement of computational model by incorporation of long-range electrostatics.
Figure 3: Exploitation of sequence-function landscapes to produce a subtype-specific hemagglutinin binder.
Figure 4: Structure and functional analysis of F-HB80.4.

Accession codes

Primary accessions

Protein Data Bank

Referenced accessions

Protein Data Bank

References

  1. Fleishman, S.J. et al. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332, 816–821 (2011).

    Article  CAS  Google Scholar 

  2. Fowler, D.M. et al. High-resolution mapping of protein sequence-function relationships. Nat. Methods 7, 741–746 (2010).

    Article  CAS  Google Scholar 

  3. Araya, C.L. & Fowler, D.M. Deep mutational scanning: assessing protein function on a massive scale. Trends Biotechnol. 435–442 (2011).

  4. Chao, G. et al. Isolating and engineering human antibodies using yeast surface display. Nat. Protoc. 1, 755–768 (2006).

    Article  CAS  Google Scholar 

  5. Cunningham, B.C. & Wells, J.A. High-resolution epitope mapping of hGH-receptor interactions by alanine-scanning mutagenesis. Science 244, 1081–1085 (1989).

    Article  CAS  Google Scholar 

  6. Bowie, J.U., Reidhaar-Olson, J.F., Lim, W.A. & Sauer, R.T. Deciphering the message in protein sequences: tolerance to amino acid substitutions. Science 247, 1306–1310 (1990).

    Article  CAS  Google Scholar 

  7. Pal, G., Kouadio, J.L., Artis, D.R., Kossiakoff, A.A. & Sidhu, S.S. Comprehensive and quantitative mapping of energy landscapes for protein-protein interactions by rapid combinatorial scanning. J. Biol. Chem. 281, 22378–22385 (2006).

    Article  CAS  Google Scholar 

  8. Bershtein, S., Segal, M., Bekerman, R., Tokuriki, N. & Tawfik, D.S. Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein. Nature 444, 929–932 (2006).

    Article  CAS  Google Scholar 

  9. Fleishman, S.J. et al. RosettaScripts: a scripting language interface to the rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).

    Article  CAS  Google Scholar 

  10. Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    Article  CAS  Google Scholar 

  11. Dutta, S. et al. Determinants of BH3 binding specificity for Mcl-1 versus Bcl-xL. J. Mol. Biol. 398, 747–762 (2010).

    Article  CAS  Google Scholar 

  12. Balakrishnan, S., Kamisetty, H., Carbonell, J.G., Lee, S.I. & Langmead, C.J. Learning generative models for protein fold families. Proteins 79, 1061–1078 (2011).

    Article  CAS  Google Scholar 

  13. Ekiert, D.C. et al. Antibody recognition of a highly conserved influenza virus epitope. Science 324, 246–251 (2009).

    Article  CAS  Google Scholar 

  14. Sui, J. et al. Structural and functional bases for broad-spectrum neutralization of avian and human influenza A viruses. Nat. Struct. Mol. Biol. 16, 265–273 (2009).

    Article  CAS  Google Scholar 

  15. Hietpas, R.T., Jensen, J.D. & Bolon, D.N. Experimental illumination of a fitness landscape. Proc. Natl. Acad. Sci. USA 108, 7896–7901 (2011).

    Article  CAS  Google Scholar 

  16. Pitt, J.N. & Ferre-D′Amare, A.R. Rapid construction of empirical RNA fitness landscapes. Science 330, 376–379 (2010).

    Article  CAS  Google Scholar 

  17. Patwardhan, R.P. et al. High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis. Nat. Biotechnol. 27, 1173–1175 (2009).

    Article  CAS  Google Scholar 

  18. Shultzaberger, R.K., Malashock, D.S., Kirsch, J.F. & Eisen, M.B. The fitness landscapes of cis-acting binding sites in different promoter and environmental contexts. PLoS Genet. 6, e1001042 (2010).

    Article  Google Scholar 

  19. Wu, X. et al. Focused evolution of HIV-1 neutralizing antibodies revealed by structures and deep sequencing. Science 333, 1593–1602 (2011).

    Article  CAS  Google Scholar 

  20. Joughin, B.A., Green, D.F. & Tidor, B. Action-at-a-distance interactions enhance protein binding affinity. Protein Sci. 14, 1363–1369 (2005).

    Article  CAS  Google Scholar 

  21. Marshall, S.A., Vizcarra, C.L. & Mayo, S.L. One- and two-body decomposable Poisson-Boltzmann methods for protein design calculations. Protein Sci. 14, 1293–1304 (2005).

    Article  CAS  Google Scholar 

  22. Throsby, M. et al. Heterosubtypic neutralizing monoclonal antibodies cross-protective against H5N1 and H1N1 recovered from human IgM+ memory B cells. PLoS ONE 3, e3942 (2008).

    Article  Google Scholar 

  23. Corti, D. 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).

    Article  CAS  Google Scholar 

  24. Efron, B., Hastie, T., Johnstone, I. & Tibshirani, R. Least angle regression. Ann. Stat. 32, 407–499 (2002).

    Google Scholar 

  25. Benatuil, L., Perez, J.M., Belk, J. & Hsieh, C.M. An improved yeast transformation method for the generation of very large human antibody libraries. Protein Eng. Des. Sel. 23, 155–159 (2010).

    Article  CAS  Google Scholar 

  26. Studier, F.W. Protein production by auto-induction in high density shaking cultures. Protein Expr. Purif. 41, 207–234 (2005).

    Article  CAS  Google Scholar 

  27. Kellogg, E.H., Leaver-Fay, A. & Baker, D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins 79, 830–838 (2011).

    Article  CAS  Google Scholar 

  28. Rohl, C.A., Strauss, C.E., Misura, K.M. & Baker, D. Protein structure prediction using Rosetta. Methods Enzymol. 383, 66–93 (2004).

    Article  CAS  Google Scholar 

  29. Sitkoff, D., BenTal, N. & Honig, B. Calculation of alkane to water solvation free energies using continuum solvent models. J. Phys. Chem. 100, 2744–2752 (1996).

    Article  CAS  Google Scholar 

  30. Sitkoff, D., Sharp, K.A. & Honig, B. Accurate calculation of hydration free-energies using macroscopic solvent models. J. Phys. Chem. 98, 1978–1988 (1994).

    Article  CAS  Google Scholar 

  31. Richards, F.M. Areas, volumes, packing, and protein-structure. Annu. Rev. Biophys. Bioeng. 6, 151–176 (1977).

    Article  CAS  Google Scholar 

  32. McCoy, A.J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

    Article  CAS  Google Scholar 

  33. Adams, P.D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  34. Emsley, P., Lohkamp, B., Scott, W.G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).

    Article  CAS  Google Scholar 

  35. Strong, M. et al. Toward the structural genomics of complexes: Crystal structure of a PE/PPE protein complex from Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA 103, 8060–8065 (2006).

    Article  CAS  Google Scholar 

  36. McDonald, I.K. & Thornton, J.M. Satisfying hydrogen-bonding potential in proteins. J. Mol. Biol. 238, 777–793 (1994).

    Article  CAS  Google Scholar 

  37. Sheriff, S., Hendrickson, W.A. & Smith, J.L. Structure of myohemerythrin in the azidomet state at 1.7/1.3-Å resolution. J. Mol. Biol. 197, 273–296 (1987).

    Article  CAS  Google Scholar 

  38. The PyMOL Molecular Graphics System, Version 1.5.0.1 Schrödinger, LLC.

  39. Chen, V.B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).

    Article  CAS  Google Scholar 

  40. Nguyen, J.T. et al. Triple combination of oseltamivir, amantadine, and ribavirin displays synergistic activity against multiple influenza virus strains in vitro. Antimicrob. Agents Chemother. 53, 4115–4126 (2009).

    Article  CAS  Google Scholar 

  41. Smee, D.F., Huffman, J.H., Morrison, A.C., Barnard, D.L. & Sidwell, R.W. Cyclopentane neuraminidase inhibitors with potent in vitro anti-influenza virus activities. Antimicrob. Agents Chemother. 45, 743–748 (2001).

    Article  CAS  Google Scholar 

  42. Nguyen, J.T. et al. Triple combination of amantadine, ribavirin, and oseltamivir is highly active and synergistic against drug resistant influenza virus strains in vitro. PLoS ONE 5, e9332 (2010).

    Article  Google Scholar 

  43. Chao, G., Cochran, J.R. & Wittrup, K.D. Fine epitope mapping of anti-epidermal growth factor receptor antibodies through random mutagenesis and yeast surface display. J. Mol. Biol. 342, 539–550 (2004).

    Article  CAS  Google Scholar 

  44. Kunkel, T.A. Rapid and efficient site-specific mutagenesis without phenotypic selection. Proc. Natl. Acad. Sci. USA 82, 488–492 (1985).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank D. Fowler and S. Fields for helpful discussions and use of their in-house software to process sequencing data, C. Lee, J. Shendure and M. Dunham for experimental expertise in DNA prep and sequencing, C. Sitz and C. Santiago for technical help and the Joint Center for Structural Genomics for crystallization using the JCSG/IAVI/TSRI Rigaku Crystalmation system. This work was funded by Defense Advanced Research Projects Agency (DARPA) and the Defense Threat Reduction Agency (DTRA), and US National Institutes of Health, National Institute of Allergy and Infectious Diseases and National Institute of General Medical Sciences. The GM/CA CAT 23-ID-B beamline has been funded in whole or in part with federal funds from National Cancer Institute (Y1-CO-1020) and NIGMS (Y1-GM-1104). Use of the Advanced Photon Source (APS) was supported by the US Department of Energy, Basic Energy Sciences, Office of Science, under contract no. DE-AC02-06CH11357. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the NIH.

Author information

Author notes

  1. Timothy A Whitehead & Sarel J Fleishman

    Present address: Present addresses: Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, USA (T.A.W.) and Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel (S.J.F.).,

  2. Timothy A Whitehead and Aaron Chevalier: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biochemistry, University of Washington, Washington, Seattle, USA

    Timothy A Whitehead, Aaron Chevalier, Yifan Song, Sarel J Fleishman, Hetunandan Kamisetty & David Baker

  2. Department of Molecular Biology and the Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California, USA

    Cyrille Dreyfus & Ian A Wilson

  3. Naval Health Research Center, San Diego, California, USA

    Cecilia De Mattos, Chris A Myers & Patrick Blair

  4. Howard Hughes Medical Institute, University of Washington, Washington, Seattle, USA

    David Baker

Authors
  1. Timothy A Whitehead
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron Chevalier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yifan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cyrille Dreyfus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sarel J Fleishman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cecilia De Mattos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chris A Myers
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hetunandan Kamisetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Patrick Blair
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ian A Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. David Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.A.W. and A.C. conceived the idea, performed yeast display selections, analyzed deep sequencing data, performed hemagglutinin binding experiments, and performed computational modeling. Y.S. developed the electrostatics model and ran computational modeling code. C.D. expressed and purified hemagglutinin proteins, determined and analyzed the crystal structures with the guidance of I.A.W., and performed hemagglutinin binding experiments. S.J.F. assisted with structural analysis and developed the computational modeling code. C.D.M. performed the viral neutralization experiments under the guidance of C.A.M. and P.B. H.K. carried out covariance analysis on deep sequencing data. D.B. conceived the idea, analyzed deep sequencing data, and developed the electrostatics model. All authors discussed the results and wrote the manuscript.

Corresponding author

Correspondence to David Baker.

Ethics declarations

Competing interests

T.A.W, S.J.F and D.B. have a patent application protecting proteins specified in this manuscript for use as potential influenza therapeutics.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1-19, Supplementary Tables 1-13 and Supplementary Scripts (PDF 7358 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Whitehead, T., Chevalier, A., Song, Y. et al. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. Nat Biotechnol 30, 543–548 (2012). https://doi.org/10.1038/nbt.2214

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.2214

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research