Deep mutational scanning of Env from the HIV-1 strain DU422 for binding to CD4 and PG16
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PROVIDER: PRJNA481765 | ENA |
REPOSITORIES: ENA
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Project description:The HIV-1 genome gains access to the inside of a cell via the mechanism of the viral spike protein Env, which undergoes a series of major conformational rearrangements after binding target receptors that ultimately drive virus-cell membrane fusion. Env is expressed as a heterogenous ensemble of conformations, which can inappropriately misdirect the host immune response towards the production of non-protective, strain-specific antibodies. Potent, broadly neutralizing antibodies (bnAbs) frequently recognize a ‘closed’ Env conformation, and therefore Env has undergone significant engineering to stabilize the closed state for vaccine incorporation. Previously, we used deep mutational scanning of Env from a prototypical tier 1 clade B strain (BaL) to characterize the sequence-activity landscape for binding to PG16, a bnAb that preferentially binds the closed state. Mutations were identified that increased expression of closed Env and reduced conformational heterogeneity, but these mutations were only partially transferable to Env sequences from other strains. To generate an expanded set of mutations that may be broadly applicable to diverse HIV-1 strains, we present here the deep mutational scanning of Env from the tier 2 clade C strain DU422 for interactions with CD4 and PG16. Residues across the trimerization domain and trimer interface have low mutational tolerance for maintaining PG16 recognition. New mutations are identified that enhance presentation of the closed Env conformation, and these are applied to Env sequences spanning multiple clades and tiers.
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Project description:HIV-1 infection begins with binding of the viral envelope glycoprotein Env to the host receptor CD4, triggering a series of conformational changes that lead to fusion of the virus and cell membranes. Env, a trimer of gp120 and gp41 subunits, occupies a ‘closed’ conformation with contacts between gp120 subunits at the apex, and transitions through an ‘open’ conformation with the gp120 subunits spread apart following CD4 binding. Using deep mutational scanning, sequence-fitness landscapes were mapped for full-length Env from the clade B BaL strain interacting with CD4, and broadly neutralizing antibodies VRC01 and PG16, which preferentially bind closed Env. Contacting residues are conserved for CD4 binding, and glycosylation at N262 is critical for accessing the high-affinity CD4-bound state. By comparison, VRC01 binding is resistant to most single amino acid substitutions, an ideal quality in a broadly neutralizing antibody. Also in contrast to CD4 interaction, Env interfacial residues are under tight selection for PG16 binding to maintain a closed conformation. Screening for mutations that enhanced PG16 binding, we identified several important sites, in particular neutralization of the electropositive apical cavity that we hypothesize promotes trimer opening by electrostatic repulsion. Mutations were combined to generate Quaternary Epitope Stabilized (QES) mutants with enhanced presentation of the PG16 epitope, and the mutations were partially transferable to other HIV-1 strains. These mutational analyses offer insight into Env conformational stabilization that may assist immunogen design.
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Project description:This dataset provides allele counts and raw fastqs for deep mutational scanning of the HIV-1 genes tat and rev when not-overlapped with one another (placed in the nef locus) as described in Fernandes et al. Functional segregation of overlapping genes in HIV Cell 2016 (in revision). Preselection (input) and post selection (replicate 1/2) files for every possible point mutant of these two HIV proteins from the NL4-3 background are given.Tab delimited files including codon counts across the amplicons are also included and are probably the most useful thing to most researchers. The data here was used to generate Figures 3 and 4 and 7 and might be of general use for people interested in deep mutational scanning, looking for signatures of epistasis in rev or tat, or reanalyzing and mining the data. FAQ: Why do the ends of each amplicon have such variation? In order to increase diversity across the flowcell, I pooled standard primers with N, NN, and NNN extensions to throw amplicons out of phase. When aligning you should trim the ends or ignore them. This means that the overlap between PE's can vary by 3 nt. Why are the filenames not easy to deal with? The filenames are tied to separate MiSeq runs. I hope to clean up the nomenclature and update this entry in the future while preserving the run information. You can get a sense of that as different residues will vary in Q-score, and that is mostly tied to the run they were pooled on and not any interesting biology. While this is makes it a little harder to follow, I think it's good to get a sense that doing this kind of analysis in high-throughput fashion leads to a reasonable amount of failure (i.e. RNA isolation, RT, fail) that led to repetition until we had good data for every position. Can you help me deal with this dataset? Yes. Please email me at jferna10@ucsc.edu, or contact me on twitter @jdf_ev. For reagents please contact Alan Frankel at frankel@cgl.ucsf.edu. Do you have the analysis scripts you used to process the data? Yes they are on github. https://github.com/nbstrauli/allele_frequency_trajectory_sim
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