Deep mutational scanning and machine learning uncover antimicrobial peptide features driving membrane selectivity

Abstract

Antimicrobial peptides commonly act by disrupting bacterial membranes, but also frequently damage mammalian membranes. Deciphering the rules governing membrane selectivity is critical to understanding their function and enabling their therapeutic use. Past attempts to decipher these rules have failed because they cannot interrogate adequate peptide sequence variation. To overcome this problem, we develop deep mutational surface localized antimicrobial display (dmSLAY), which reveals comprehensive positional residue importance and flexibility across an antimicrobial peptide sequence. We apply dmSLAY to Protegrin-1, a potent yet toxic antimicrobial peptide, and identify thousands of sequence variants that positively or negatively influence its antibacterial activity. Further analysis reveals that avoiding large aromatic residues and eliminating disulfide bound cysteine pairs while maintaining membrane bound secondary structure greatly improves Protegrin-1 bacterial specificity. Moreover, dmSLAY datasets enable machine learning to expand our analysis to include over 5.7 million sequence variants and reveal full Protegrin-1 mutational profiles driving either bacterial or mammalian membrane specificity. Our results describe an innovative, high-throughput approach for elucidating antimicrobial peptide sequence-structure-function relationships which can inform synthetic peptide-based drug design.

Figure 1:. Protegrin-1 deep mutational SLAY predicts residue importance and flexibility.

A) Surface localized antimicrobial display expresses an OmpA fusion protein tethering the PG-1 library to the outer membrane (OM). Induction of display results in cell death for variants maintaining antimicrobial activity (red-active, blue-inactive). change in reads between induced and uninduced cultures can predict antimicrobial potential. B) Scatter plot of induced versus uninduced reads for the entire PG-1 library on log scales. 1:1 and the native PG-1 sequence (PG-1.0) read ratios are shown. C) Scatter plot of MIC for a subset of 52 variants in the PG-1 library versus the log₂-fold change in reads. D) Table charting dmSLAY predictions for all single PG-1 variants in the library. The native PG-1 sequence and position in columns one and two, amino acid change at each position going across the top categorized by side chain. PG-1 secondary structure and disulfide bonds are diagramed to the left. Position column is color coded by average log₂-fold change. Bold boxed cells were evaluated in vitro. An X marks an incorrect dmSLAY prediction.

Figure 2.. Changes in secondary structure correlate with Protegrin-1 lytic activity.

Circular dichroism spectra select PG-1 variants from the dmSLAY scan color coded by minimum inhibitory concentration (A, B) or percent hemolysis (C, D). The native PG-1 sequence (PG-1.0) spectrum is shown as a dotted line. Arrows indicate different trends in cell membrane lysis for PG-1.37 (A, C) or tryptophan containing variants with a maximum near 230 nm. All spectra are the average of technical triplicate.

Figure 3.. Membrane selectivity is influenced by aromatics and loss of cysteine pairs.

A) Bar graph comparing selectivity score (median minimum inhibitory concentration * average percent hemolysis) for PG-1 variants confirmed to be antibacterial on a log scale. B) Circular dichroism spectra of same PG-1 variants color coded by selectivity score. Spectra are the average of technical triplicate. C) Scatter plot of PG-1 variant aggregation in relative fluorescent units (RFUs) on a log scale versus linear percent hemolysis. Each data point is the mean of triplicate reactions, trendline represents a linear fit of log transformed aggregation data versus hemolysis with R² = 0.63. D) Bar chart of selectivity score for a subset of mutations found in the four most selective PG-1 variants on a log scale. Brackets indicate where disulfide bonds are present in the native PG-1 sequence.

Figure 4.. Protegrin-1 variants demonstrate strong specificity for bacterial membranes.

A) Bar chart of propidium iodide (PI) uptake measured in relative fluorescent units (RFUs) for E. coli cells treated with the indicated concentration of each PG-1 variant. B) Kill curve showing colony forming units (CFUs) present over time for cultures treated with 8 μg/ml peptide. C) Percentage of bacterial killing and hemolysis observed in co-culture of E. coli and human red blood cells treated with increasing concentrations of PG-1 variants after one hour of treatment. All data points in this figure are the mean of triplicate reactions. Error bars represent one standard deviation.

Figure 5.. Machine learning identifies mutational profiles promoting membrane specificity.

A) Venn diagram showing the three machine learning models trained on PG-1 variant data and the cut off used to identify the 17,348 most bacterially specific (A) or mammalian specific (C) from over 5.7 million one, two and three residue variants. Mutational heat maps charting the number of times each mutation is observed within the bacterially selective group (B) or mammalian selective group (D). The native PG-1 sequence is shown going down the far left column and specific residue change across the top categorized by side chain. Native PG-1 secondary structure is diagramed to the left.