Optimization of the antimicrobial peptide Bac7 by deep mutational scanning

Abstract

Background: Intracellularly active antimicrobial peptides are promising candidates for the development of antibiotics for human applications. However, drug development using peptides is challenging as, owing to their large size, an enormous sequence space is spanned. We built a high-throughput platform that incorporates rapid investigation of the sequence-activity relationship of peptides and enables rational optimization of their antimicrobial activity. The platform is based on deep mutational scanning of DNA-encoded peptides and employs highly parallelized bacterial self-screening coupled to next-generation sequencing as a readout for their antimicrobial activity. As a target, we used Bac7_1-23, a 23 amino acid residues long variant of bactenecin-7, a potent translational inhibitor and one of the best researched proline-rich antimicrobial peptides.

Results: Using the platform, we simultaneously determined the antimicrobial activity of >600,000 Bac7_1-23 variants and explored their sequence-activity relationship. This dataset guided the design of a focused library of ~160,000 variants and the identification of a lead candidate Bac7PS. Bac7PS showed high activity against multidrug-resistant clinical isolates of E. coli, and its activity was less dependent on SbmA, a transporter commonly used by proline-rich antimicrobial peptides to reach the cytosol and then inhibit translation. Furthermore, Bac7PS displayed strong ribosomal inhibition and low toxicity against eukaryotic cells and demonstrated good efficacy in a murine septicemia model induced by E. coli.

Conclusion: We demonstrated that the presented platform can be used to establish the sequence-activity relationship of antimicrobial peptides, and showed its usefulness for hit-to-lead identification and optimization of antimicrobial drug candidates.

Keywords: Antibiotics; Antimicrobial peptides; Antimicrobial resistance; Antimicrobials; Deep mutational scanning; Drug discovery; High-throughput screening; Proline-rich antimicrobial peptides; Protein synthesis inhibitor; Sequence-activity relationship.

Fig. 1

Sequence-activity relationship of Bac7_1-23. a DMS workflow. epPCR: the Bac7_1-23 gene is amplified at a high error rate using an error-prone DNA polymerase in the presence of Mn²⁺. Cloning: the mutagenized DNA sequences are inserted into plasmids downstream of inducible promoters. Transformation: E. coli TOP10 is transformed with the generated peptide-encoding DNA library. Growth: the pooled transformants are grown in a single shaking flask (n = 3), peptide synthesis is induced and plasmids are isolated after 4 h. NGS: the abundance of each peptide-encoding DNA sequence is determined by NGS at the time of induction and 4 h later. Analysis: for each peptide-encoding DNA sequence, the log2-fold change is determined (log2 ratio of abundances at the two time points). Histogram showing the log2-fold changes of the abundance of the peptide-encoding DNA of all 601,551 variants. Ranking: peptide sequences are ranked by the degree of the observed antimicrobial effect. The more negative a log2-fold change, the higher the observed antimicrobial effect and vice versa. b Bac7_1-23 sequence-activity relationship displaying the magnitude of the observed antimicrobial effect. For each amino acid residue substitution (and stop codon), the enrichment in higher or lower antimicrobial peptides is determined and a z-score (z) is calculated (see the “Methods” section). z corresponds to the number of standard deviations by which the calculated enrichment lies above (positive values) or below (negative values) the mean a null distribution indicating no enrichment. z is empirically divided into four groups, corresponding to very positive (yellow; z ≥ 40), positive (green; z ≥ 4), negative (blue; z ≤ -4), or very negative (purple; z ≤ −40) effects on the antimicrobial activity. No effect on growth inhibition is detectable if the z is close to 0 (white; −4 < z < 4). Black dots are used for indication of Bac7_1-23 wild-type amino acid residues. The underlined positions are chosen as targets for the subsequent site-saturation mutagenesis.

Fig. 2

DMS of Bac7_1-23 site-saturation mutagenesis library. a Enrichment curves. Peptides are first ranked according to their antimicrobial activity (x-axis; from left to right starting from the most growth inhibitory) and then a running enrichment score for each amino acid residue at each of the four substitution sites is calculated (y-axis). Increasing y-values indicate the presence of that particular amino acid residue in the ranking segment while decreasing y-values indicate the absence. In all cases, the AUC is calculated, whereby positive AUC values represent an enrichment among more active peptides (left side of the x-axis) and negative AUC values represent an enrichment among less active peptides (right side of the x-axis). An example is shown for the glutamate (E) at position 5 (AUC_E5). b AUC values for each amino acid residue substitution. Effects on antimicrobial activity are binned empirically: very positive (yellow; AUC ≥ 0.2), positive (green; AUC ≥ 0.07), no effect (white; −0.07 < AUC < 0.07 = interquartile range of all values), negative (blue; AUC ≤ −0.07), or very negative (purple; AUC ≤ −0.2). Black dots correspond to the Bac7 parental amino acid residue at each position

Fig. 3

Effect of amino acid residue double combinations. a Examples of double amino acid residue combinations resulting in small (top), large positive (middle), and large negative (bottom) ∆AUC values. Left: to calculate the AUC of a single amino acid residue substitution (AUC_AA1), all peptides are ranked on the horizontal axis according to their antimicrobial activity (as shown in Fig. 2a). Middle: the AUC of the same residue substitution is recalculated for the subset of peptides with a fixed second AA residue at another position (AUC_{AA1 | AA2}). Right: the influence of the second amino acid on the effect on antimicrobial activity of the first amino acid is calculated by subtracting the two previous calculations, resulting in ∆AUC. Indicators ~ and ^ are used to link the values to Fig. 3b. b ∆AUC values for all 4800 amino acid residue combinations. For each of the 20 amino acid residues at each of the four positions, there are 60 (20 amino acid residues at the remaining three positions) combinations with a second amino acid residue. Non-additive effects are estimated if a second amino acid residue changes the effect that a first amino acid residue has on antimicrobial activity, resulting in either larger positive or negative ∆AUC values. Cooperativity is measured when ∆AUC is larger than 0.09. Antagonism is measured when ∆AUC is smaller than −0.09 (=outliers of a boxplot containing all results; IQR ± 1.5 *IQR). Exemplary ∆AUC values: *P19 and C18, ~F19 and Y18, ^P19 and P18

Fig. 4

Characterization of Bac7PS and Bac7_1-23. a MICs of a panel of clinical isolates of E. coli (n=45) including MDR bacteria (ESBL, CRE, n = 25). MIC₅₀ of Bac7_1-23 = 7.5 μM, MIC₅₀ of Bac7PS = 2.9 μM. b Membrane damage assays measuring GFP loss (% of cells that lost GFP fluorescence) and PI uptake (% cells that gained PI fluorescence) when incubating E. coli TOP10 in MHB II for 30 min in the presence of increasing concentrations of Bac7PS and Bac7_1-23 (n = 3; error bars = 1SD). MIC of melittin against E. coli TOP10 is 5.0 μM (data not shown). c In vitro translation inhibition assays against E. coli ATCC 29522 (left) and HEK 293 ribosomes (right). IC₅₀ values are extracted from a luminescence assay translating firefly luciferase mRNA peptide concentrations between 800 and 0.08 μM (n = 9). p-values (p) are calculated by performing a Wilcoxon rank-sum test, testing for differences in mean IC₅₀ values of Bac7_1-23 and Bac7PS

Fig. 5

Efficacy of Bac7PS in a murine model. a Efficacy study scheme. Drugs are applied to mice infected with E. coli ATCC 25922. Bacteria and drugs are administered intraperitoneally (IP). b Survival rates after infection. Mice are infected with E. coli ATCC 25922 and then treated with CIP as positive control and without a drug (vehicle) as the negative control. The study was performed for each condition as described in a