Prion sequence curation and peptide mining
We curated 2,897 UniProt entries annotated with prion keyword (keyword: KW-0640; accessed 24 October 2023), encompassing classical prion proteins as well as prion-related domain–containing proteins. Those included reviewed, unreviewed and isoform entries. Encrypted peptides were defined as substrings of 8–50 amino acid residues derived from these proteins. We were able to obtain 19,324,138 unique candidate encrypted peptides.
APEX 1.1
Antimicrobial activity was predicted using APEX 1.1, an updated version of our previously published APEX deep learning framework for peptide antibiotic discovery. In brief, peptide sequences were encoded in APEX 1.1 using the same feature-representation strategy described for the original APEX framework. Each peptide was treated as an amino acid sequence with added start and terminal symbols, and each residue was represented by its corresponding AAindex descriptor vector rather than by one-hot encoding. AAindex provides a 566-dimensional representation capturing diverse physicochemical and biochemical properties of amino acids. Unknown or non-amino acid symbols were represented as zero vectors. Sequences were then zero padded to a fixed maximum length (52 tokens including start and terminal symbols) to generate matrix-form inputs for the neural network. APEX 1.1 then uses a peptide-sequence encoder based on recurrent and attention neural network components to generate a learned hidden representation of each peptide sequence, which is then processed by downstream fully connected neural networks for antimicrobial prediction. In our framework, one prediction head was trained on an in-house dataset to perform multitask regression of species-specific MIC values (bacterial strains include A. baumannii ATCC 19606, E. coli ATCC 11775, E. coli AIC221, E. coli AIC222, K. pneumoniae ATCC 13883, P. aeruginosa PAO1, P. aeruginosa PA14, S. aureus ATCC 12600, methicillin-resistant S. aureus (MRSA) ATCC BAA−1556, vancomycin-resistant E. faecalis ATCC 700802 and vancomycin-resistant E. faecium ATCC 700221), whereas a second prediction head was trained on public AMP and non-AMP data to perform AMP/non-AMP classification, thereby improving representation learning through data augmentation. The downstream fully connected networks were implemented as four-layer architectures with layer normalization, rectified linear unit activation and dropout, and ensemble learning was used to improve prediction robustness. Our model was trained on in-house dataset comprising 1,642 peptides and 15,718 MIC measurements across 11 pathogenic strains, together with 19,564 public AMPs and 9,857 non-AMPs. For inactive measurements exceeding the highest tested concentration, MIC values were set to 512 μmol l−1. Hyperparameter selection followed the original APEX framework and final predictions were obtained by averaging the selected top-performing ensemble models. Full architectural details, model development and benchmarking have been described previously10,11. We used APEX 1.1 to predict the antimicrobial activity of 19,324,138 unique peptide fragments derived from prion proteomes. For each peptide, APEX 1.1 generated predicted MIC values against 11 pathogenic bacterial strains. To obtain an overall measure of predicted antimicrobial potency for ranking, we calculated the median predicted MIC across the 11 strains for each peptide. Peptides with a median predicted MIC of ≤64 μmol l−1 were designated as candidate prionins.
Physicochemical properties analyses
The eight physicochemical properties of peptides, including normalized hydrophobic moment, normalized hydrophobicity, net charge, isoelectric point, disordered conformation propensity, propensity to aggregation in vitro, linear moment and amphiphilicity index, were obtained from the DBAASP server13. Note that Eisenberg and Weiss scale19 was chosen as the hydrophobicity scale.
Phylogenetic tree visualization
To obtain the phylogenetic tree, the taxon IDs of 139 organisms containing candidate prionins were uploaded to NCBI Taxonomy Common Tree (ref. 20).
Peptide sequence similarity
We followed the previous practice10 to use local alignment to calculate pairwise protein sequence similarity. Let LA(i, j) represent the optimal alignment score between protein i and protein j, a similarity score between this protein pair can be expressed as \(\frac{\mathrm{LA}(i,\,j)\,}{\sqrt{\mathrm{LA}\left(i,i\right)\times \mathrm{LA}(j,\,j)\,}}\).
Peptide sequence space visualization
For (1) predicted antimicrobial prion encrypted peptides, (2) peptides from our in-house dataset and (3) AMPs curated from DBAASP13, DRAMP 3.021 and APD314. We calculated pairwise protein sequence similarity and used Uniform Manifold Approximation and Projection (UMAP) to transform the whole sequence similarity matrix into a two-dimensional space. This reduced space is interpretated as a peptide sequence space, allowing us to visualize the how peptides from different sources distribute in it.
Prionin sequences selection
For 1,179 prionins having ≤64 μmol l−1 median MIC by APEX 1.1 prediction, we sorted them by median MIC increasingly and applied the following filtering: (1) the selected peptide should have <70% sequence similarity to all in-house peptides and publicly available AMPs, where the latter ones came from the union of AMPs from DBAASP, APD3 and DRAMP 3.0; and (2) the selected peptides themselves should have <70% sequence similarity. If two peptides break this rule, we keep the more active one. To evaluate the effect of more stringent diversity filters, we also retrospectively applied 50% and 25% sequence-similarity thresholds to the selected candidate set, which would have retained 47 and 0 peptides, respectively.
Peptide synthesis
All peptides used in the experiments were purchased from AAPPTec and synthesized by solid-phase peptide synthesis using the Fmoc strategy.
Bacterial strains and growth conditions
In this study, we used the following pathogenic bacterial strains obtained from the American Type Culture Collection (ATCC): A. baumannii ATCC 19606, E. coli ATCC 11775, K. pneumoniae ATCC 13883, P. aeruginosa PAO1, P. aeruginosa PA14, S. aureus ATCC 12600, S. aureus ATCC BAA-1556 (methicillin-resistant strain), E. faecalis ATCC 700802 (vancomycin-resistant strain) and E.s faecium ATCC 700221 (vancomycin-resistant strain). E. coli AIC221 (E. coli MG1655 phnE_2::FRT (control strain for AIC222)) and E. coli AIC222 (E. coli MG1655 pmrA53 phnE_2::FRT (polymyxin resistant; colistin-resistant strain)) were kindly donated by Prof. Mark Goulian (University of Pennsylvania). Pseudomonas Isolation (P. aeruginosa strains) agar plates were exclusively used in the case of Pseudomonas species. All the other pathogens were grown in Luria-Bertani (LB) broth and on LB agar. In all the experiments, bacteria were inoculated from one-isolated colony and grown overnight (16 h) in liquid medium at 37 °C. The following day, inoculums were diluted 1:100 in fresh media and incubated at 37 °C to mid-logarithmic phase.
RBCs and human embryonic kidney cells
Human embryonic kidney (HEK293T) cells were obtained from the ATCC (CRL-3216). RBCs and human serum were purchased from Zen-Bio. The RBC samples were obtained from the same certified healthy donor (blood type A−).
MIC determination
Broth microdilution assays were performed to determine the MIC values of each peptide. Peptides were added to nontreated polystyrene microtiter 96-well plates and 2-fold serially diluted in sterile water from 1 to 64 μmol L−1. Bacterial inoculum at 4 × 106 CFU mL−1 in LB medium was mixed 1:1 with the peptide. The MIC was defined as the lowest concentration of peptide able to completely inhibit the bacterial growth after 24 h of incubation at 37 °C. All assays were done in three independent replicates.
Circular dichroism experiments
The circular dichroism experiments were conducted using a J-1500 circular dichroism spectropolarimeter (Jasco) in the Biological Chemistry Resource Center at the University of Pennsylvania. Experiments were performed at 25 °C, the spectra graphed are an average of three accumulations obtained with a quartz cuvette with an optical path length of 1.0 mm, ranging from 260 to 190 nm at a rate of 50 nm min−1 and a bandwidth of 0.5 nm. The concentration of all peptides tested was 50 μmol l−1, and the measurements were performed in water, a mixture of trifluoroethanol (TFE) and water in a 3:2 ratio, a mixture of methanol (MeOH) and water in a 1:1 ratio, and sodium dodecyl sulfate (SDS) in water at 10 mmol l−1, with respective baselines recorded before measurement. A Fourier transform filter was applied to minimize background effects. Secondary structure fraction values were calculated using the single spectra analysis tool on the server BeStSel22.
Outer-membrane permeabilization assays
The NPN uptake assay was used to evaluate the ability of the peptides to permeabilize the bacterial outer membrane. Inocula of E. coli AIC221 were grown to an OD at 600 nm of 0.4, centrifuged (9,391g for 3 min), washed and resuspended in 5 mmol l−1 HEPES buffer (pH 7.4) containing 5 mmol l−1 glucose. The bacterial solution was added to a white 96-well plate (100 μl per well) together with 4 μl of NPN at 0.5 mmol l−1. Consequently, peptides diluted in water were added to each well and the fluorescence was measured at λex = 350 nm and λem = 420 nm over time for 45 min. The relative fluorescence was calculated using the untreated control (buffer + bacteria + fluorescent dye) as baseline and the following equation was applied to reflect the percentage difference between the baselines and the sample:
$$\begin{array}{l}{\rm{Percentage}}\,{\rm{difference}}\\ =\displaystyle \frac{100\times ({{\rm{fluorescence}}}_{{\rm{sample}}}-{{\rm{fluorescence}}}_{{\rm{untreated}}\,{\rm{control}}})}{{{\rm{fluorescence}}}_{{\rm{untreated}}\,{\rm{control}}}}.\end{array}$$
Cytoplasmic-membrane depolarization assays
The cytoplasmic-membrane depolarization assay was performed using the membrane potential-sensitive dye DiSC3-5. E. coli AIC221 in the mid-logarithmic phase was washed (9,391g for 3 min) and resuspended at 0.05 OD ml−1 (optical value at 600 nm) in HEPES buffer (pH 7.2) containing 20 mmol l−1 glucose and 0.1 mol l−1 KCl. DiSC3-5 at 20 μmol l−1 was added to the bacterial suspension (100 μl per well) for 15 min to stabilize the fluorescence, which indicates the incorporation of the dye into the bacterial membrane, and then the peptides were mixed 1:1 with the bacteria to a final concentration corresponding to their MIC values. Membrane depolarization was then followed by reading changes in the fluorescence (λex = 622 nm and λem = 670 nm) over time for 60 min. The relative fluorescence was calculated using the untreated control (buffer + bacteria + fluorescent dye) as baseline and the following equation was applied to reflect the percentage difference between the baselines and the sample:
$$\begin{array}{l}{\rm{Percentage}}\,{\rm{difference}}\\ =\displaystyle \frac{100\times ({{\rm{fluorescence}}}_{{\rm{sample}}}-{{\rm{fluorescence}}}_{{\rm{untreated}}\,{\rm{control}}})}{{{\rm{fluorescence}}}_{{\rm{untreated}}\,{\rm{control}}}}.\end{array}\,\,$$
Haemolytic activity assays
To evaluate the release of haemoglobin from human erythrocytes upon treatment of each of the encrypted peptides, human RBCs were obtained from Zen-Bio (male donor, blood type A−) obtained from heparin anticoagulated blood. RBCs were washed with PBS (pH 7.4) four times by centrifugation at 800g for 10 min. Aliquots of 200-fold diluted cells (75 μl) were mixed with peptide solution (0.78–100 μmol l−1; 75 μl), and the mixture was incubated for 4 h at room temperature. After the incubation, the plate was centrifuged at 1,300g for 10 min to precipitate cells and debris, and 100 μl of supernatant from each well were transferred to a new 96-well plate for absorbance reading (405 nm) using an automatic plate reader. The percentage of haemolysis was defined by comparison with negative control (samples containing PBS) and positive control (samples containing 1% (v/v) SDS in PBS solution).
$$\begin{array}{l}{\rm{Haemolysis}}({\rm{ \% }})\\ =\displaystyle \frac{100\times ({{\rm{Absorbance}}}_{405{\rm{nm}}\,{\rm{peptide}}}-{{\rm{Absorbance}}}_{405{\rm{nm}}\,{\rm{negative}}\,{\rm{control}}})}{({{\rm{Absorbance}}}_{405{\rm{nm}}\,{\rm{positive}}\,{\rm{control}}}-{{\rm{Absorbance}}}_{405{\rm{nm}}\,{\rm{negative}}\,{\rm{control}}})}.\end{array}$$
Cytotoxicity assays
The cells were cultured in high-glucose Dulbecco’s modified Eagle’s medium supplemented with 1% penicillin and streptomycin (antibiotics) and 10% fetal bovine serum and grown at 37 °C in a humidified atmosphere containing 5% CO2.
One day before the experiment, 100 μl aliquots of human embryonic kidney (HEK293T) cells, at a concentration of 50,000 cells per ml, were seeded into each well of 96-well plates (5,000 cells per well). Following cell attachment, the HEK293T cells were treated with increasing concentrations of peptides (ranging from 8 to 128 μmol l−1) and incubated for 24 h. After the exposure period, cytotoxicity was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Specifically, the MTT reagent was prepared at a concentration of 0.5 mg ml−1 in phenol red-free medium and used to replace the peptide-containing supernatants (100 μl per well). The plates were then incubated for 4 h at 37 °C in a humidified atmosphere with 5% CO2, facilitating the formation of insoluble formazan crystals. These crystals were subsequently dissolved in 0.04 mol l−1 hydrochloric acid prepared in anhydrous isopropanol. Absorbance was measured at 570 nm using a spectrophotometer to quantify cell viability. All experiments were conducted in triplicate (three biological replicates).
Skin abscess infection mouse model
The back of 6-week-old female CD-1 mice under anaesthesia were shaved and injured with a superficial linear skin abrasion made with a needle. An aliquot of A. baumannii ATCC 19606 (8.33 × 105 c.f.u. ml−1; 20 μl) previously grown in LB medium until 0.5 OD ml−1 (optical value at 600 nm) and then washed twice with sterile PBS (pH 7.4, 9,391g for 3 min) added to the scratched area. Peptides diluted in sterile water at their MIC value were administered to the wounded area 1 h postinfection. At 2 and 4 days postinfection, animals were euthanized and a uniform excision of the scarified skin was excised, homogenized using a bead beater (25 Hz for 20 min), tenfold serially diluted and plated on McConkey agar plates for c.f.u. quantification. The experiments were performed using six mice per group. Mice were single housed to avoid cross-contamination and maintained under a 12-h light/dark cycle at 22 °C with humidity controlled at 50%. The skin abscess infection mouse model was revised and approved by the University Laboratory Animal Resources from the University of Pennsylvania (protocol no. 806763).
Quantification and statistical analysisReproducibility of the experimental assays
All assays were performed in three independent biological replicates as indicated in each figure legend and in the relevant Methods sections. The values obtained for haemolytic and cytotoxic activity were estimated by nonlinear regression based on the screen of peptides in a gradient of concentrations and represent the haemolytic and cytotoxic concentration values needed to lyse and kill 50% of the cells present in the experiment. In the skin abscess mouse model, we used six mice per group following established protocols approved by the University Laboratory of Animal Resources of the University of Pennsylvania.
Statistical tests
In the mouse experiments, all the raw data were log10 transformed and the statistical significance was determined using one-way analysis of variance (ANOVA) followed by Dunnett’s test. All the P values are shown for each of the groups, and all groups were compared with the untreated control group.
Statistical analysis
All calculations and statistical analyses of the experimental data were conducted using GraphPad Prism v.11. Statistical significance between different groups was calculated using the tests indicated in each figure legend. No statistical methods were used to predetermine sample size.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.