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A simple method for incorporating sequence information into directed evolution experiments
 

A simple method for incorporating sequence information into directed evolution experiments

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    A simple method for incorporating sequence information into directed evolution experiments A simple method for incorporating sequence information into directed evolution experiments Presentation Transcript

    • A simple method for incorporating sequence information into directed evolution experiments Kyle L. Jensen*, Hal Alper*, Curt Fischer, Gregory Stephanopoulos Department of Chemical Engineering Massachusetts Institute of Technology sequence phenotype
    • When screening throughput is limit, linking sequence to phenotype can help direct downstream searches
        • Screen based
        • (selectable trait)‏
        • Assay based
        • (no selectable trait)‏
    • Here, a P Ltet promoter was mutated to create a library of promoter variants Alper H., C. Fischer, E. Nevoigt, and G. Stephanopoulos, 2005. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. U S A 102:12678-83.
    • 69 promoter variants were created using error prone PCR
    • The 69 promoter variants spanned an 800-fold range of activity - How different are the underlying, mutagenized sequences? - What, on a sequence level, causes the variation? 800 fold range Log relative fluorescence Mutant number Top 50% Bottom 50%
    • Each of the 69 mutants had a unique sequence and incorporated multiple transition SNPs mutations promoter region Log relative fluorescence Mutant number Position [nt] Mutant number
    • The effects of individual mutations were “masked” by the presence of other mutations
      • Just because a mutation occurs more frequently in one class, is it correlated?
      • Is the ratio of top/bottom important?
      • What is the statistical significance of a mutation that is distributed between the two classes?
      Some mutations have obvious effects ...most do not Position [nt] Mutant number Class distribution
    • Each individual position can be evaluated using a simple binomial distribution Same as: what's the probability of getting heads 14 of 20 coin tosses? P-value: 14 or more heads out of 20 Assuming the positions are independent Position [nt] Class distribution
    • Similar analysis over the promoter region revealed 7 positions significantly correlated with activity Class distribution Position [nt]
    • Position [nt] Mutant number Class distribution Log relative fluorescence Mutant number Position [nt]
    • A similar analysis can be applied to an arbitrary number of mutants and phenotypic classes 1 2 M . . . mutants M phenotypes Mutants with mutations as “position 35” . . . . . . . . . or 1 2 3 4 5 6 Y
    • The generalized probability of the phenotype distribution can be used to find mutation-phenotype correlations
      • Probability of a particular vector color distribution
      • Significance of a correlation between mutations at “position 35” and the green phenotypic class
        • Prior probability of
        • green phenotype
    • In our case, we tested 8 locations, spanning a range of functions & confidences Class distribution Position [nt]
    • 7/8 of the single position mutants were in agreement with the predicted function
    • Rationally designed promoters with combinations of mutations showed predicted activity but also signs of site interaction
        • *
    • In summary, this simple method, based on multinomial statistics, can be used to link sequence variations to particular phenotypes