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

By Timothy A. Whitehead, A. Chevalier, Yifan Song, C. Dreyfus, Sarel Jacob Fleishman, C. D. de Mattos, C. A. Myers, Hetunandan Kamisetty, P. Blair, Ian A. Wilson and 1 more. 2012

387 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Evaluating Protein Transfer Learning with TAPE (2019, background): “Improvement on these tasks range in application, including designing new antibodies [8], improving cancer diagnosis [9], and ﬁnding new antimicrobial genes hiding in the so-called “Dark Proteome”: tens of millions of sequences with no labels where existing techniques for determining protein…”
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Antibody Recognition of the Pandemic H1N1 Influenza Virus Hemagglutinin Receptor Binding Site (2013, background): “The antibody-antigen interactions can also be recapitulated through proteins engineered to target the HA RBS, as has been successfully performed against the HA stem (44, 45).”
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Emerging themes in the computational design of novel enzymes and protein–protein interfaces (2013, background): “There are two complementary routes that design efforts are undertaking to address these challenges: one is to improve the underlying force-ﬁeld to encompass a more accurate treatment of electrostatics and solvation [55].”
Community‐wide evaluation of methods for predicting the effect of mutations on protein–protein interactions (2013, background): “14 Briefly, single-point mutant variants were created, corresponding to all 20 amino acids at each of 53 and 45 positions for the computationally designed influenza binders HB36.”
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