The emergence of drug-resistant bacteria calls for the discovery of new antibiotics. Yet, for decades, traditional discovery strategies have not yielded new classes of antimicrobial. Here, by mining the human proteome via an algorithm that relies on the sequence length, net charge, average hydrophobicity and other physicochemical properties of antimicrobial peptides, we report the identification of 2,603 encrypted peptide antibiotics, many of which are encoded in proteins with biological function unrelated to the immune system. We show that the encrypted peptides kill pathogenic bacteria by targeting their membrane, modulate gut and skin commensals, do not readily select for bacterial resistance, and possess anti-infective activity in skin abscess and thigh infection mouse models. We also show, in vitro and in the two mouse models of infection, that encrypted antibiotic peptides from the same biogeographical area display synergistic antimicrobial activity. Our algorithmic strategy allows for the rapid mining of proteomic data and opens up new routes for the discovery of candidate antibiotics.
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The main data supporting the results in this study are available within the paper and its Supplementary Information. The list of the identified encrypted peptides, and source data for the normalized abundance of genes encoding different protein classes, for amino acid frequency in encrypted peptides compared with known AMPs, for expression levels of proteins containing encrypted peptides, and for antimicrobial activity (in vitro and in vivo), as well as data on synergistic interactions, evolution of resistance and mechanism of action, are provided with this paper. Source data are provided with this paper.
The custom Python code for scanning the human proteome to detect candidate encrypted peptides is available as Supplementary Information.
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C.d.l.F.-N. holds a Presidential Professorship at the University of Pennsylvania, is a recipient of the Langer Prize by the AIChE Foundation and acknowledges funding from the Institute for Diabetes, Obesity, and Metabolism, the Penn Mental Health AIDS Research Center of the University of Pennsylvania, the Nemirovsky Prize, and the Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania. Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM138201 and the Defense Threat Reduction Agency (DTRA; HDTRA11810041 and HDTRA1-21-1-0014). The authors also acknowledge resource and expertise contributions from Simon Knight and Elizabeth Grice at the University of Pennsylvania, and support by the Burroughs Wellcome Fund PATH Award (Principal investigator (PI), Elizabeth Grice), the Linda Pechenik Montague Investigator Award (Perelman School of Medicine; PI, Grice), and the Penn Skin Biology and Diseases Resource-based Center (NIH/NIAMS P30AR069589; PI, Grice). L.F. was supported by the Dermatology Research Training Grant (NIH/NIAMS T32AR007465; PIs, Grice and Margolis). Mark Goulian kindly donated E. coli AIC221 and AIC222. All figures were prepared using the Biorender drawing toolkit. The authors thank E. Broset and all members of the de la Fuente Lab for insightful discussions.
The authors declare no competing interests.
Peer review information Nature Biomedical Engineering thanks Mohan Babu, Kim Lewis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Schematic of the biogeographic region within the human body where proteins containing encrypted peptides are located. Expression levels are displayed in a gradient; organs in blue indicate high expression levels and organs in red, low expression levels. b, Normalized expression level values expressed in log10 intensity based absolute quantification (iBAQ), a commonly used metric for protein abundance47.
Extended Data Fig. 2 Synergy between encrypted peptides found within the same area of the human body.
a, Experimental layout of the 96-well plates used for two-way synergy experiments using pairs of encrypted peptides. The following encrypted peptides were used: apelin-36, apelin receptor early endogenous 1, natriuretic peptide, big dynorphin, FIBa-GVV27, vWF-PQR19, SRFP1-KKI32, SRFP1-FAL48, INTb-FTR26, SCUB1-SKE25, SCUB3-KHK26, and SCUB3-MLP22. Briefly, two-fold dilutions ranging from 0 to 50 μmol L-1 of the peptide solutions were plated in 96-well plates and 106 bacterial cells in NB were added to each well to reach a final volume of 200 μL. b, The FIC value, which indicates the degree of synergy between two antimicrobial agents against a target microorganism (in this case, P. aeruginosa PAO1) was calculated based on the MICs of the peptides used alone and in combination. FIC index values ≤0.5 indicate synergy; additive effects are captured by 0.5≤FIC≤1; 1≤FIC≤4 indicates indifference; and FIC index ≥4 represents antagonism. Assays were performed in three independent replicates.
Extended Data Fig. 3 Membrane permeabilization and depolarization assays for several bacterial strains.
a, Outer membrane permeabilization experiments showed that encrypted peptides (SCUB1-SKE25, SCUB3-KHK26 and natriuretic peptide) permeabilized the outer membranes of P. aeruginosa PAO1, B. fragilis ATCC25285, and S. epidermidis as much as they permeabilized the A. baumannii ATCC19606 outer membrane (Fig. 3e). b, Cytoplasmic membrane depolarization assays performed against P. aeruginosa PAO1 and the gut commensal B. fragilis ATCC25285. As shown for A. baumannii ATCC19606 (Fig. 3d), the encrypted peptides did not depolarize the cytoplasmic membrane. Data in a and b are the mean ± s.d. Assays were performed in three independent replicates.
Extended Data Fig. 4 Anti-infective activity and synergistic interactions of encrypted peptides against P. aeruginosa PAO1 in a neutropenic thigh infection mouse model.
a, SCUB1-SKE25 (25 μmol L-1; 77.9 μg mL-1) and SCUB3-MLP22 (25 μmol L-1; 66.9 μg mL-1) showed inhibitory activity against P. aeruginosa PAO1, especially when used in combination at their MIC (obtained from in vitro synergy experiments; 3.12 and 6.25 μmol L-1, respectively). b, Mouse weight was monitored throughout the duration of the neutropenic thigh infection model (8 days) and under all conditions tested to rule out potential toxic effects mediated by the encrypted peptides. The statistical significance in a was determined using one-way ANOVA, ***p < 0.001, ****p < 0.0001, features on the violin plots represent median and upper and lower quartiles. The data in b are the mean ± s.d. Eight mice were used per group.
Extended Data Fig. 5 Anti-infective activity of encrypted peptides in a neutropenic thigh infection mouse model.
Treatment with 4-fold MIC of SCUB1-SKE25 (100 μmol L-1; 311.6 μg mL-1) and SCUB3-MLP22 (100 μmol L-1; 267.6 μg mL-1) alone and in combination (at 12.5 and 25 μmol L-1, respectively). Both monotherapy and combination therapy displayed similar antimicrobial activity against A. baumannii ATCC19606 to treatment groups using the MIC of each peptide. Polymyxin B and levofloxacin were used as controls, the former of which completely cleared the infection. The statistical significance was determined using one-way ANOVA, ****p < 0.0001, features on the violin plots represent median and upper and lower quartiles. Four mice were used per group.
Supplementary tables and figures.
List of encrypted peptides found in the human proteome according to our physicochemical features-based scoring function.
Source data for the Supplementary figures.
Python code for scanning the human proteome to detect candidate encrypted peptides.
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Torres, M.D.T., Melo, M.C.R., Flowers, L. et al. Mining for encrypted peptide antibiotics in the human proteome. Nat Biomed Eng 6, 67–75 (2022). https://doi.org/10.1038/s41551-021-00801-1
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