Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design

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Science  29 Nov 2019:
Vol. 366, Issue 6469, pp. 1139-1143
DOI: 10.1126/science.aaw2900
  • Fig. 1 Measurement of all single AAV2 capsid mutations in a multiplexed viral production assay.

    (A) Assay and calculation of production fitness (s′). (B) Barcode frequencies: plasmid (fp) versus virus (fv). (C) Fitness for WT replicates and stop codons in VP1, VP2, and VP3. (D) Fitness for all single–amino-acid insertions, deletions (Δ), stop codons (*), and substitutions. Radius is from capsid center. VR, variable regions. (E) Average fitness for insertions at each position colored on the 3D structure. The triangle is the 3-fold axis and the pentagon is the 5-fold axis. (F) Fitness distributions split by conservation and location within or outside of variable regions. In all panels, *p < 10−20 (Mann–Whitney U test).

  • Fig. 2 A frameshifted protein expressed from the VP1 region functions through competitive exclusion.

    (A) Discovery of MAAP, a frameshifted ORF in the VP1 region. Top: Production fitness for mutations with stop codons in the +1 frame (red) and for cap codons synonymous to the red points but without creating stops in the +1 frame (gray). Solid lines indicate the 10-position moving average. Bottom: p-value for the observed difference in +1 frame stops and non-stops for the moving window of 10 positions. (B) Western blot of MAAP-3xFLAG with M2 anti-FLAG HRP antibody and an anti-GAPDH loading control. (C) Membrane association: confocal imaging of MAAP-GFP localization for AAV serotypes 2, 5, 8, and 9. Blue is membrane stain and green is green fluorescent protein. (D) Deleterious effects on production for MAAP stop codons relative to other synonymous codons in the cap gene, supplying in trans: pRep, pRep+MAAP, or pRep+MAAP negative controls. *p < 10−5 (Mann–Whitney U test). (E) MAAP mutants produce at levels similar to WT when expressed individually (top) are outcompeted by WT in a head-to-head format, but then rescued by pRep+MAAP in trans (bottom). **p < 0.05, ***p < 0.01 (one-way t-test).

  • Fig. 3 Multiplexed measurement of in vivo biodistribution reveals phenotypic clustering and structural design principles.

    (A) Biodistribution assay. (B) In vivo selection values for validation mutants in library format versus individual assays. (C) Projection of individual mutants onto PC1 and PC2 derived from PCA, colored by tissue enrichment. (D) Top: Highlighting k-means clusters of mutants with enhanced tissue targeting. Bottom: Mutant biodistribution values within each cluster. (E) Position of cluster mutants in capsid structure, rendering first all residues and then only residues from each cluster to show the importance of buried residues for cluster 3.

  • Fig. 4 Machine-guided design of AAV capsids outperforms random mutagenesis.

    (A) Left: Generation of multimutants from individual amino-acid liver-biodistribution measurements (black dots: WT). Mutation probability distributions for each position are calculated from single–amino-acid mutant fitness (top). Right: Sampled mutations are combined to generate a multimutant target variant and then all mutants from WT to target are synthesized and experimentally measured (top shows ordering by increasing fitness). (B) Top: Fraction of designed mutants with liver biodistribution values greater than WT for random (gray) and machine-guided design mutants (orange). Bottom: Distribution of biodistribution values for random and designed mutants separated by number of mutations. Even at distances farther than four mutations, the designed approach outperforms random mutagenesis (pink box).

Supplementary Materials

  • Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design

    Pierce J. Ogden, Eric D. Kelsic, Sam Sinai, George M. Church

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    • Materials and Methods 
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