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

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.

Figure 1.

Overview of MITE. Oligonucleotides encoding all desired amino acid substitutions in a template open reading frame (ORF) are synthesized in two pools. The oligonucleotides in each pool are organized into non-overlapping tiles with shared 5′- and 3′-ends that facilitate selective PCR amplification. After amplification, each tile is inserted into a linearized expression vector that contains the remainder of the ORF using sequence- and ligation-independent cloning techniques.

Figure 2.

Mutational scanning of APH(3′)II using kanamycin selection. (a) Visual representation of the changes in abundance of each specific amino acid (ΔAA) at each position after selection at four different concentrations. The color in each matrix entry corresponds to the change relative to the input library. The known secondary structure of APH(3′)II is shown for reference, including alpha helices (gold), beta sheet strands (yellow) and beta hairpins (red; the first 10 residues are unstructured). The highly deleterious effect of proline substitutions, which impose unique structural constraints, stand out at low concentrations. (b) Projections of the observed changes in the abundance of mutant amino acids (ΔMut) onto a crystal structure of APH(‘3)II in complex with kanamycin (PDB accession: 1ND4). Numerical values are provided in Supplementary Data 2.

Figure 3.

Identification of specificity-determining residues in APH(3′)II. (a–e) The positions of substitutions that are specifically tolerated (+) or depleted (−) under selection with the indicated aminoglycoside relative to selection with kanamycin are shown in red. The identities of the substitutions are listed in Supplementary Tables S1 and S2. Arrows indicate a rotation of the structure. (f) Specificity toward the indicated aminoglycoside for pairs of synthetic enzyme variants designed to favor (+) or disfavor (−) this substrate over kanamycin, relative to that of WT APH(3′)II. Bars show the medians and error bars show the ranges observed over 2–3 independent cultures, bounded by the resolution of the assay (2-fold dilutions; see also Supplementary Table S2). The variant predicted to favor kanamycin over amikacin (Ami.−) showed minimal activity on both substrates. (g) Optical density in Escherichia coli cultures transformed with WT APH(3′)II or synthetic variants designed to specifically favor paromomycin (Paro+) or kanamycin (Paro−), after selection with each of these two aminoglycosides.