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. 2012 May 27;30(6):543-8.
doi: 10.1038/nbt.2214.

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

Affiliations

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

Timothy A Whitehead et al. Nat Biotechnol. .

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.

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Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare competing financial interests: details are available in the online version of the paper.

Figures

Figure 1
Figure 1
Sequence-function landscapes of designed influenza-binding proteins. (a,b) Deep sequencing yields large numbers of independent observations to robustly determine enrichment values in stringent binding selections to the H1 hemagglutinin subtype. Mutations that are heavily depleted are shown in green, whereas beneficial mutations are indicated in red. Horizontal dashed lines indicate 100 sequence counts for unique nonsynonymous substitutions in the library, whereas vertical dashed lines demarcate the enrichment ratio of the starting sequence, showing that most substitutions are neutral to deleterious. (a, HB80.3 library; b, HB36.4 library). (c,d) Model of H1 hemagglutinin (shown in blue ribbons) bound to HB80.3 (c) and HB36.4 (d). The designed binding proteins are colored by positional Shannon entropy with green indicating positions of low entropy and red those of high entropy. Gray ribbons on HB36.4 indicate positions without deep sequencing data. (e,f) Heat maps representing H1 hemagglutinin-binding enrichment values under stringent binding selection for all possible single mutations in all 51 positions of HB80.3 (e) and in 53/93 positions of HB36.4 (f). Starting residue identities are shown in white font, and the central helix paratope for the design variants is colored in orange in the secondary structure diagrams above the heat maps. Positions with enrichment greater than fourfold are colored yellow and were included in the subsequent designed library and black boxes around positions indicate hot-spot residues in the original designs.
Figure 2
Figure 2
Improvement of computational model by incorporation of long-range electrostatics. (a,b) Correlation between calculated probability of binding Pbinding and the enrichment value improves when the Rosetta energy function is supplemented with a long-range electrostatics model. To highlight the effect of the electrostatic term, only mutations to charged residues (Arg, Lys, Asp and Glu) are shown. Mutations to neutral residues show a similar correlation; however, there is little difference with and without the electrostatic term. HB36.4 (a) and HB80.3 (b); open blue squares, all-atom Rosetta energy function without the electrostatics term; red closed circles, energy function supplemented with electrostatic interactions computed using the fixed electrostatic field of the target hemagglutinin. (c,d) Electrostatic potential from H1 hemagglutinin (blue ribbons) mapped onto model of HB36.4 (c) and HB80.3 (d). HB36.4 substitutions A37K, Q40K, P65K and P69K improve electrostatic interactions with hemagglutinin. HB80.3 substitutions G12K, A35K and S42K improve electrostatic interactions with hemagglutinin.
Figure 3
Figure 3
Exploitation of sequence-function landscapes to produce a subtype-specific hemagglutinin binder. (a) The enrichment values for medium stringency binding of HB36.4 to H1 and H5 HA (Supplementary Table 2) are correlated as expected for epitopes that only differ by a few mutations. The vertical and horizontal lines indicate enrichment for the starting sequence. The mutation I58E was selected because it is neutral in the H1 binding population but depleted in the H5 binding population. (b) Yeast surface display titrations of HB36.4 (squares) and HB36.4 I58E (circles) against the H1 hemagglutinin subtype (dashed line/open symbols) or H5 hemagglutinin subtype (solid line/closed symbols) shows that HB36.4 I58E selectively binds the H1 subtype.
Figure 4
Figure 4
Structure and functional analysis of F-HB80.4. (a) Superposition of the crystal structure of F-HB80.4-SC1918/hemagglutinin complex and the design model. The F-HB80.4 is represented in orange, SC1918 HA1 subunit in gold, HA2 subunit in cyan and the computational design in green. Superposition was performed using the HA2 subunits. For clarity, only the hemagglutinin from the crystal structure is depicted here (the hemagglutinin used for superposition of the design, which is essentially identical to the crystal structure, was omitted). (b) Close-up view of the F-HB80. 4-SC1918/hemagglutinin interface with the key hemagglutinin-contacting residues labeled. The main contact helix on F-HB80.4 is well ordered, and after refinement electron density was apparent for most of the key contact residues on F-HB80.4, including Phe13, Ile17, Ile21, Phe25 and Tyr40. A total of 1,460 Å2 is buried at the interface with hemagglutinin, similar to the surface area buried by CR6261. The coloring is the same and F-HB80.4 is oriented as in a. (c) Phylogenetic tree showing the relationships between the 16 hemagglutinin subtypes and a summary of F-HB80.4 binding. Green ticks indicate positive binding by F-HB80.4 and red crosses no binding. Subtypes that have not been tested for binding are indicated in black. (d) Plot of cytopathic effect (CPE) reduction versus F-HB80.4 concentration for seasonal flu virus A/H1N1/Hawaii/31/2007 (blue diamonds, top panel) and pandemic A/California/04/2009(H1N1) virus (red diamonds, bottom panel). Green squares are controls for cell viability at each F-HB80.4 concentration tested. Error bars represent a 95% confidence interval in the measurement. The calculated EC50 of F-HB80.4 for A/H1N1/Hawaii/31/2007 and pandemic A/California/04/2009(H1N1) viruses is 98 nM (0.9 μg/ml) and 170 nM (1.6 μg/ml), respectively.

Comment in

References

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