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

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.

Authors:
; ; ; ; ; ; ; ; ; ;  [1];  [2];  [2]
  1. (UWASH)
  2. (
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Org.:
OTHERNIHNIGMSNIAID
OSTI Identifier:
1043086
Resource Type:
Journal Article
Journal Name:
Nature Biotechnology
Additional Journal Information:
Journal Volume: 30; Journal Issue: 6; Journal ID: ISSN 1087-0156
Country of Publication:
United States
Language:
ENGLISH
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; AFFINITY; DESIGN; HEMAGGLUTININS; INFLUENZA; MONOCLONAL ANTIBODIES; OPTIMIZATION; PROTEINS; SPECIFICITY; VIRUSES

Citation Formats

Whitehead, Timothy A., Chevalier, Aaron, Song, Yifan, Dreyfus, Cyrille, Fleishman, Sarel J., De Mattos, Cecilia, Myers, Chris A., Kamisetty, Hetunandan, Blair, Patrick, Wilson, Ian A., Baker, David, Scripps), and NRL). Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. United States: N. p., 2012. Web. doi:10.1038/nbt.2214.
Whitehead, Timothy A., Chevalier, Aaron, Song, Yifan, Dreyfus, Cyrille, Fleishman, Sarel J., De Mattos, Cecilia, Myers, Chris A., Kamisetty, Hetunandan, Blair, Patrick, Wilson, Ian A., Baker, David, Scripps), & NRL). Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing. United States. doi:10.1038/nbt.2214.
Whitehead, Timothy A., Chevalier, Aaron, Song, Yifan, Dreyfus, Cyrille, Fleishman, Sarel J., De Mattos, Cecilia, Myers, Chris A., Kamisetty, Hetunandan, Blair, Patrick, Wilson, Ian A., Baker, David, Scripps), and NRL). Tue . "Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing". United States. doi:10.1038/nbt.2214.
@article{osti_1043086,
title = {Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing},
author = {Whitehead, Timothy A. and Chevalier, Aaron and Song, Yifan and Dreyfus, Cyrille and Fleishman, Sarel J. and De Mattos, Cecilia and Myers, Chris A. and Kamisetty, Hetunandan and Blair, Patrick and Wilson, Ian A. and Baker, David and Scripps) and NRL)},
abstractNote = {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.},
doi = {10.1038/nbt.2214},
journal = {Nature Biotechnology},
issn = {1087-0156},
number = 6,
volume = 30,
place = {United States},
year = {2012},
month = {6}
}