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

Academic Article
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Overview

authors

  • Whitehead, T. A.
  • Chevalier, A.
  • Song, Y. F.
  • Dreyfus, C.
  • Fleishman, S. J.
  • De Mattos, C.
  • Myers, C. A.
  • Kamisetty, H.
  • Blair, P.
  • Wilson, Ian
  • Baker, D.

publication date

  • June 2012

journal

  • Nature Biotechnology  Journal

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.

subject areas

  • Animals
  • Antiviral Agents
  • Cell Survival
  • Computational Biology
  • Dogs
  • Drug Discovery
  • Hemagglutinin Glycoproteins, Influenza Virus
  • High-Throughput Nucleotide Sequencing
  • Influenza A Virus, H1N1 Subtype
  • Madin Darby Canine Kidney Cells
  • Models, Molecular
  • Neutralization Tests
  • Protein Binding
  • Static Electricity
  • Thermodynamics
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Identity

PubMed Central ID

  • PMC3638900

International Standard Serial Number (ISSN)

  • 1087-0156

Digital Object Identifier (DOI)

  • 10.1038/nbt.2214

PubMed ID

  • 22634563
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Additional Document Info

start page

  • 543

end page

  • 8

volume

  • 30

issue

  • 6

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