Abstract

Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins, ribonucleic acid and deoxyribonucleic acid. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently known, however, about the degree to which such fitness landscapes are dependent on the specific assay conditions from which they are inferred. To explore this issue, we performed comprehensive single-substitution mutational scanning of APH(3')II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics, in Escherichia coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting local fitness landscapes showed significant dependence on both antibiotic structure and concentration, and that this dependence can be exploited to guide protein engineering. Specifically, we found that differential analysis of fitness landscapes allowed us to generate synthetic APH(3')II variants with orthogonal substrate specificities.

Submission Details

ID: XKe3Fnvw

Submitter: Shu-Ching Ou

Submission Date: Feb. 11, 2019, 4:25 p.m.

Version: 1

Publication Details

Additional Information

WT amino acid (AA) counts/differences are calculated as the mean values of WT counts/differences at all positions. Refer to the assay detail for the definition of amino acid differences. For assay naming: S=Selection round 1, 2, 3, or 4. D=Drug and dilution, eg. Kan12 refers to Kanamycin at 1:2 WT MIC. L=Input library 1 or 2. Estimated 1:1 WT MICs: 225 μg/mL for kanamycin (Kan), 2500 μg/mL for ribostamycin (Ribo), 5 μg/mL for G418, 10 μg/mL for amikacin (Ami), 40 μg/mL for neomycin (Neo) and 320 μg/mL for paromomycin (Paro).