Deep mutational scanning has emerged as a promising tool for mapping sequence-activity relationships in proteins, RNA and DNA. In this approach, diverse variants of a sequence of interest are first ranked according to their activities in a relevant pooled assay, and this ranking is then used to infer the shape of the fitness landscape around the wild-type sequence. Little is currently know, 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 deep mutational scanning of APH(3')II, a Tn5 transposon-derived kinase that confers resistance to aminoglycoside antibiotics, in E. coli under selection with each of six structurally diverse antibiotics at a range of inhibitory concentrations. We found that the resulting fitness landscapes showed significant dependence on both antibiotic structure and concentration. This shows that the notion of essential amino acid residues is context-dependent, but also 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.

Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes