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High-throughput mutagenesis reveals functional determinants for DNA targeting by activation-induced deaminase.

Gajula KS, Huwe PJ, Mo CY, Crawford DJ, Stivers JT, Radhakrishnan R, Kohli RM - Nucleic Acids Res. (2014)

Bottom Line: To rationalize these functional requirements, we performed molecular dynamics simulations that suggest that AID and its hyperactive variants can engage DNA in multiple specific modes.These findings align with AID's competing requirements for specificity and flexibility to efficiently drive antibody maturation.Beyond insights into the AID-DNA interface, our Sat-Sel-Seq approach also serves to further expand the repertoire of techniques for deep positional scanning and may find general utility for high-throughput analysis of protein function.

View Article: PubMed Central - PubMed

Affiliation: Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

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Related in: MedlinePlus

Characterization of hyperactive AID variants. (A) Kinetics for deamination with hyperactive AID variants. Deamination assays were carried out with 32P end-labeled 27-mer substrate containing a single cytosine in an AGC hotspot. The rate of product formation was determined at various substrate concentrations and the mean and standard deviation from at least three replicates are shown for each condition. The curves were fit to the Michaelis–Menten equation and the determined values for Vmax and Km are reported. (B) AID-WT, R119G and cvBEST were assayed against an array of substrates that contained a single cytosine in 1 of 16 XXC sequence contexts, where X = A, mC, G or T. The percent product formation for each substrate was determined and the overall preferences at the -2 and -1 positions were determined based on the averages across each position (Supplementary Figure S6). The preferences at the -2 and -1 positions are represented as a pie chart for A, mC, G or T.
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Figure 5: Characterization of hyperactive AID variants. (A) Kinetics for deamination with hyperactive AID variants. Deamination assays were carried out with 32P end-labeled 27-mer substrate containing a single cytosine in an AGC hotspot. The rate of product formation was determined at various substrate concentrations and the mean and standard deviation from at least three replicates are shown for each condition. The curves were fit to the Michaelis–Menten equation and the determined values for Vmax and Km are reported. (B) AID-WT, R119G and cvBEST were assayed against an array of substrates that contained a single cytosine in 1 of 16 XXC sequence contexts, where X = A, mC, G or T. The percent product formation for each substrate was determined and the overall preferences at the -2 and -1 positions were determined based on the averages across each position (Supplementary Figure S6). The preferences at the -2 and -1 positions are represented as a pie chart for A, mC, G or T.

Mentions: As our method selects for enhanced deaminase activity, we chose two AID variants for additional detailed analysis: the R119G point mutant and the sequence selected in the covariation experiment (cvBEST, D118A/R119G/K120R/A121R). In both the rifampin mutagenesis assay and in the in vitro deamination assay, cvBEST showed enhanced activity relative to AID-WT (Figure 4A). The majority of these enhancements were attributable to the R119G mutation, although some additional enhancement did arise from the mutations in cvBEST. Steady-state kinetic analysis revealed an enhancement in Vmax for the mutant variants relative to AID-WT, with roughly similar Km values (Figure 5A). The rate determining step in deamination by AID has not been established and thus mutations could alter substrate binding, the chemical steps in catalysis or product release. Importantly, the agreement between the rifampin assay and in vitro experiments suggest that the same step is impacted in both settings.


High-throughput mutagenesis reveals functional determinants for DNA targeting by activation-induced deaminase.

Gajula KS, Huwe PJ, Mo CY, Crawford DJ, Stivers JT, Radhakrishnan R, Kohli RM - Nucleic Acids Res. (2014)

Characterization of hyperactive AID variants. (A) Kinetics for deamination with hyperactive AID variants. Deamination assays were carried out with 32P end-labeled 27-mer substrate containing a single cytosine in an AGC hotspot. The rate of product formation was determined at various substrate concentrations and the mean and standard deviation from at least three replicates are shown for each condition. The curves were fit to the Michaelis–Menten equation and the determined values for Vmax and Km are reported. (B) AID-WT, R119G and cvBEST were assayed against an array of substrates that contained a single cytosine in 1 of 16 XXC sequence contexts, where X = A, mC, G or T. The percent product formation for each substrate was determined and the overall preferences at the -2 and -1 positions were determined based on the averages across each position (Supplementary Figure S6). The preferences at the -2 and -1 positions are represented as a pie chart for A, mC, G or T.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4150791&req=5

Figure 5: Characterization of hyperactive AID variants. (A) Kinetics for deamination with hyperactive AID variants. Deamination assays were carried out with 32P end-labeled 27-mer substrate containing a single cytosine in an AGC hotspot. The rate of product formation was determined at various substrate concentrations and the mean and standard deviation from at least three replicates are shown for each condition. The curves were fit to the Michaelis–Menten equation and the determined values for Vmax and Km are reported. (B) AID-WT, R119G and cvBEST were assayed against an array of substrates that contained a single cytosine in 1 of 16 XXC sequence contexts, where X = A, mC, G or T. The percent product formation for each substrate was determined and the overall preferences at the -2 and -1 positions were determined based on the averages across each position (Supplementary Figure S6). The preferences at the -2 and -1 positions are represented as a pie chart for A, mC, G or T.
Mentions: As our method selects for enhanced deaminase activity, we chose two AID variants for additional detailed analysis: the R119G point mutant and the sequence selected in the covariation experiment (cvBEST, D118A/R119G/K120R/A121R). In both the rifampin mutagenesis assay and in the in vitro deamination assay, cvBEST showed enhanced activity relative to AID-WT (Figure 4A). The majority of these enhancements were attributable to the R119G mutation, although some additional enhancement did arise from the mutations in cvBEST. Steady-state kinetic analysis revealed an enhancement in Vmax for the mutant variants relative to AID-WT, with roughly similar Km values (Figure 5A). The rate determining step in deamination by AID has not been established and thus mutations could alter substrate binding, the chemical steps in catalysis or product release. Importantly, the agreement between the rifampin assay and in vitro experiments suggest that the same step is impacted in both settings.

Bottom Line: To rationalize these functional requirements, we performed molecular dynamics simulations that suggest that AID and its hyperactive variants can engage DNA in multiple specific modes.These findings align with AID's competing requirements for specificity and flexibility to efficiently drive antibody maturation.Beyond insights into the AID-DNA interface, our Sat-Sel-Seq approach also serves to further expand the repertoire of techniques for deep positional scanning and may find general utility for high-throughput analysis of protein function.

View Article: PubMed Central - PubMed

Affiliation: Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Show MeSH
Related in: MedlinePlus