Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning

Abstract

Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.

Keywords: Denisovan; Neanderthal; antibiotic resistance; antibiotics; antimicrobial peptides; drug discovery; hominins; machine learning; mouse models; protein engineering.

Fig. 1.. Computational-experimental framework for molecular de-extinction of antimicrobial peptides.

Panel (A) demonstrates the computational proteolysis pipeline, where user-defined proteins are processed into 8-residue subsequences that are classified as cleavage and non-cleavage sites. Input proteins are then tokenized at predicted cleavage sites, and the resulting fragments can be filtered by user-defined curation methods. Curation methods can include machine learning-based activity prediction, human expert curation, or other methods. Successes in archaic and modern proteome mining are visualized in panel (B), where precursors were computationally digested to reveal encrypted antimicrobial subsequences. The pipeline concludes with in vitro (C) and in vivo (D) experimental validation of fragment bioactivity, including proteolytic degradation assays, MoA assays, and mouse weight monitoring as a proxy for host toxicity. Figure created with BioRender.com and the PyMOL Molecular Graphics System, Version 2.1 Schrödinger, LLC.

Fig. 2.. Model performance and antimicrobial peptide data distributions.

Panels describe panCleave random forest performance evaluation (a-h) and physicochemical distributions for positive hits (i–l). Optimized panCleave random forest performance is reported for independent test data ($n=\mathrm{9,927}$): (a) accuracy-probability threshold tradeoff curves, comparing accuracy per estimated probability of class membership; (b) the receiver operating characteristic curve; (c) precision-recall curve; (d) panCleave test accuracy for proteases with at least 100 test observations; (e) panCleave test accuracy by protease catalytic type; (f) accuracy of panCleave relative to pre-existing models for three caspases (panCleave in red); (g) positive hit rate by fragment curation method; and (h) positive hit rate by antimicrobial activity classifier. Panels i–l compare amino acid frequency (i), fragment length (j), normalized hydrophobicity (k), and net charge distributions (l) for MEPs, AEPs, and AMPs reported in DBAASP. Hydrophobicity scores employ the Eisenberg and Weiss scale. Note that DBAASP data were restricted to fragments of length 8–40 residues for length, hydrophobicity, and charge distributions, with null values excluded. DBAASP amino acid frequencies were computed by excluding noncanonical residues.

Fig. 3.. Antimicrobial activity, resistance to enzymatic degradation, and mechanism of action of modern and archaic EPs.

(a) Antimicrobial activity of the EPs. Briefly, a fix number of 10⁶ bacterial cells per mL⁻¹ was used in all the experiments. The modern and archaic EPs were two-fold serially diluted ranging from 128 to 2 μmol L⁻¹ in a 96-wells plate and incubated at 37 °C for one day. After the exposure period, the absorbance of each well was measured at 600 nm. Untreated solutions were used as controls and minimal concentration values for complete inhibition were presented as a heat map of antimicrobial activities (μmol L⁻¹) against nine pathogenic bacterial strains. All the assays were performed in three independent replicates and the heat map shows the mode obtained within the two-fold dilutions concentration range studied. (b) Schematic of the resistance to enzymatic degradation experiment, where peptides were exposed for a total period of six hours to fetal bovine serum that contains several active proteases. Aliquots of the resulting solution were analyzed by liquid chromatography coupled to mass spectrometry. (c) Modern and (d) archaic peptides had different degradation behaviors. In summary, archaic peptides are more resistant to enzymatic degradation than modern peptides. Experiments were performed in two independent replicates. (e) Schematic showing the behavior of 1-(N-phenylamino)naphthalene (NPN) the fluorescent probe used to indicate membrane permeabilization caused by the EPs. (f) Modern and (g) archaic EPs fluorescence values relative to the untreated control showing that MEPs are more efficient at permeabilizing the outer membrane of A. baumannii cells than polymyxin B (PMB) and archaic EPs. (h) Schematic of how 3,3′-dipropylthiadicarbocyanine iodide [DiSC₃-(5)], a hydrophobic fluorescent probe, was used to indicate membrane depolarization caused by the EPs. (i) Modern and (j) archaic EPs fluorescence values relative to the untreated control showing that archaic peptides are much stronger depolarizers of the cytoplasmic membrane of A. baumannii cells than polymyxin B (PMB) and modern EPs. Experiments were performed in three independent replicates. Figure created with BioRender.com and the PyMOL Molecular Graphics System, Version 2.1 Schrödinger, LLC.

Fig. 4.. Anti-infective activity of modern and archaic EPs in pre-clinical animal models.

(a) Schematic of the skin abscess mouse model used to assess the anti-infective activity of the modern and archaic EPs with activity against A. baumannii cells. (b) Peptides were tested at their MIC in a single dose one hour after the establishment of the infection. Each group consisted of six mice (n = 6) and the bacterial loads used to infect each mouse derived from a different inoculum. (c) To rule out toxic effects of the peptides, mouse weight was monitored throughout the whole extent of the experiment. (d) Schematic of the neutropenic thigh infection mouse model in which bacteria is injected intramuscularly in the right thigh and modern and archaic EPs were administered intraperitoneally to assess their systemic anti-infective activity. Mice were euthanized six and eight days after the beginning of the experiment, i.e., two- and four-days post infection. Each group consisted of four mice (n = 4) and the bacterial loads used to infect each mouse derived from a different inoculum. (e) All EPs, except TKN1-SSI17, showed bacteriostatic activity inhibiting proliferation of bacteria. Peptides with bacteriostatic activity were able to maintain their effect during the entire experiment (eight days), except for A7E2T1-SPR39 that was effective for six days. (f) Mouse weight was monitored throughout the duration of the neutropenic thigh infection model (8 days total) to rule out potential toxic effects of cyclophosphamide injections, bacterial load, and the EPs. The antibiotic polymyxin B was used as positive control in both models. Statistical significance in b and e (day 6) was determined using one-way ANOVA, and in e (day 8) using Kruskal-Wallis test because of the non-normal distribution and unequal variance across groups; p values are shown for each of the groups, all groups were compared to the untreated control group; features on the violin plots represent median and upper and lower quartiles. Data in c and f are the mean plus and minus the standard deviation. Figure created with BioRender.com and the PyMOL Molecular Graphics System, Version 2.1 Schrödinger, LLC.