CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters

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  • Centers for Disease Control and Prevention. Antimicrobial Resistance https://www.cdc.gov/antimicrobial-resistance/index.html (2019).

  • National Academies. Examining the Long-Term Health and Economic Effects of Antimicrobial Resistance in the United States https://www.nationalacademies.org/our-work/examining-the-long-term-health-and-economic-effects-of-antimicrobial-resistance-in-the-united-states (2022).

  • Tong, S. Y. C., Fowler, V. G., Jr., Skalla, L. & Holland, T. L. Management of Staphylococcus aureus bacteremia: a review. JAMA https://doi.org/10.1001/jama.2025.4288 (2025).

  • Centers for Disease Control and Prevention. Infection Control Guidance: Preventing Methicillin-Resistant Staphylococcus aureus (MRSA) in Healthcare Facilities https://www.cdc.gov/mrsa/hcp/infection-control/index.html (2025).

  • From the Centers for Disease Control and Prevention. Vancomycin resistant Staphylococcus aureus—Pennsylvania, 2002. JAMA 288, 2116 (2002).


    Google Scholar
     

  • Conlon, B. P. et al. Persister formation in Staphylococcus aureus is associated with ATP depletion. Nat. Microbiol. 1, 16051 (2016).

  • Otto, M. Staphylococcal biofilms. Microbiol. Spectr. 6, https://doi.org/10.1128/microbiolspec.GPP3-0023-2018 (2018).

  • Ganesan, N., Mishra, B., Felix, L. & Mylonakis, E. Antimicrobial peptides and small molecules targeting the cell membrane of Staphylococcus aureus. Microbiol. Mol. Biol. Rev. 87, e0003722 (2023).


    Google Scholar
     

  • Xuan, J. et al. Antimicrobial peptides for combating drug-resistant bacterial infections. Drug Resist. Updat. 68, 100954 (2023).


    Google Scholar
     

  • Lewis, K. Persister cells. Annu Rev. Microbiol. 64, 357–372 (2010).


    Google Scholar
     

  • Mishra, B., Reiling, S., Zarena, D. & Wang, G. Host defense antimicrobial peptides as antibiotics: design and application strategies. Curr. Opin. Chem. Biol. 38, 87–96 (2017).


    Google Scholar
     

  • Kim, W. et al. A selective membrane-targeting repurposed antibiotic with activity against persistent methicillin-resistant Staphylococcus aureus. Proc. Natl. Acad. Sci. USA 116, 16529–16534 (2019).


    Google Scholar
     

  • Panahi Chegini, P., Nikokar, I., Tabarzad, M., Faezi, S. & Mahboubi, A. Effect of amino acid substitutions on biological activity of antimicrobial peptide: design, recombinant production, and biological activity. Iran. J. Pharm. Res. 18, 157–168 (2019).


    Google Scholar
     

  • Mishra, B. & Wang, G. Ab initio design of potent anti-MRSA peptides based on database filtering technology. J. Am. Chem. Soc. 134, 12426–12429 (2012).


    Google Scholar
     

  • Vora, L. K. et al. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics 15, 1916 (2023).

  • Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M. & Ahsan, M. J. Machine learning in drug discovery: a review. Artif. Intell. Rev. 55, 1947–1999 (2022).


    Google Scholar
     

  • Bhadra, P., Yan, J., Li, J., Fong, S. & Siu, S. W. I. AmPEP: sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci. Rep. 8, 1697 (2018).


    Google Scholar
     

  • Meher, P. K., Sahu, T. K., Saini, V. & Rao, A. R. Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Sci. Rep. 7, 42362 (2017).


    Google Scholar
     

  • Pinacho-Castellanos, S. A., Garcia-Jacas, C. R., Gilson, M. K. & Brizuela, C. A. Alignment-free antimicrobial peptide predictors: improving performance by a thorough analysis of the largest available data set. J. Chem. Inf. Model 61, 3141–3157 (2021).


    Google Scholar
     

  • Li, C. et al. AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics 23, 77 (2022).


    Google Scholar
     

  • de la Lastra, J. M. P., Wardell, S. J. T., Pal, T., de la Fuente-Nunez, C. & Pletzer, D. From data to decisions: leveraging artificial intelligence and machine learning in combating antimicrobial resistance—a comprehensive review. J. Med. Syst. 48, 71 (2024).


    Google Scholar
     

  • Santos-Junior, C. D. et al. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 187, 3761–3778.e3716 (2024).

  • Cesaro, A. et al. Synthetic antibiotic derived from sequences encrypted in a protein from human plasma. ACS Nano 16, 1880–1895 (2022).


    Google Scholar
     

  • Mishra, B. et al. Antimicrobial peptide developed with machine learning sequence optimization targets drug resistant Staphylococcus aureus in mice. J. Clin. Invest. https://doi.org/10.1172/JCI185430 (2025).

  • Yoshida, M. et al. Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides. Chem.-Us 4, 533–543 (2018).


    Google Scholar
     

  • Zhang, H. et al. Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides. J. Adv. Res. https://doi.org/10.1016/j.jare.2024.02.016 (2024).

  • Xia, X., Torres, M. D. T. & de la Fuente-Nunez, C. Proteasome-derived antimicrobial peptides discovered via deep learning. Preprint at bioRxiv https://doi.org/10.1101/2025.03.17.643752 (2025).

  • Wan, F., Torres, M. D. T., Guan, C. & de la Fuente-Nunez, C. Tutorial: guidelines for the use of machine learning methods to mine genomes and proteomes for antibiotic discovery. Nat. Protoc. 20, 2685–2697 (2025).

  • Porto, W. F., Pires, A. S. & Franco, O. L. CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides. PLoS One 7, e51444 (2012).


    Google Scholar
     

  • Kim, W. et al. A new class of synthetic retinoid antibiotics effective against bacterial persisters. Nature 556, 103–107 (2018).


    Google Scholar
     

  • Umetani, M. et al. Observation of persister cell histories reveals diverse modes of survival in antibiotic persistence. Elife 14, e79517 (2025).

  • Luro, S., Potvin-Trottier, L., Okumus, B. & Paulsson, J. Isolating live cells after high-throughput, long-term, time-lapse microscopy. Nat. Methods 17, 93–100 (2020).


    Google Scholar
     

  • Conlon, B. P. et al. Activated ClpP kills persisters and eradicates a chronic biofilm infection. Nature 503, 365–370 (2013).


    Google Scholar
     

  • Nishi, H., Komatsuzawa, H., Fujiwara, T., McCallum, N. & Sugai, M. Reduced content of lysyl-phosphatidylglycerol in the cytoplasmic membrane affects susceptibility to moenomycin, as well as vancomycin, gentamicin, and antimicrobial peptides, in Staphylococcus aureus. Antimicrob. Agents Chemother. 48, 4800–4807 (2004).


    Google Scholar
     

  • Green, B. N. et al. Methicillin-resistant Staphylococcus aureus: an overview for manual therapists(). J. Chiropr. Med. 11, 64–76 (2012).


    Google Scholar
     

  • Souli, M. et al. Changing characteristics of Staphylococcus aureus bacteremia: results from a 21-year, prospective, longitudinal study. Clin. Infect. Dis. 69, 1868–1877 (2019).


    Google Scholar
     

  • CDC. Centers for Disease Control and Prevention. Vital Signs https://www.cdc.gov/vitalsigns/staph/index.html (2019).

  • Lewis, K. Riddle of biofilm resistance. Antimicrob. Agents Chemother. 45, 999–1007 (2001).


    Google Scholar
     

  • Liu, C. et al. Clinical practice guidelines by the Infectious Diseases Society of America for the treatment of methicillin-resistant Staphylococcus aureus infections in adults and children: executive summary. Clin. Infect. Dis. 52, 285–292 (2011).


    Google Scholar
     

  • Wan, F. & de la Fuente-Nunez, C. Mining for antimicrobial peptides in sequence space. Nat. Biomed. Eng. 7, 707–708 (2023).


    Google Scholar
     

  • Huang, J. et al. Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nat. Biomed. Eng. 7, 797–810 (2023).


    Google Scholar
     

  • Maasch, J., Torres, M. D. T., Melo, M. C. R. & de la Fuente-Nunez, C. Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell Host Microbe 31, 1260–1274 e1266 (2023).


    Google Scholar
     

  • Torres, M. D. T. et al. Mining for encrypted peptide antibiotics in the human proteome. Nat. Biomed. Eng. 6, 67–75 (2022).


    Google Scholar
     

  • Hurdle, J. G., O’Neill, A. J., Chopra, I. & Lee, R. E. Targeting bacterial membrane function: an underexploited mechanism for treating persistent infections. Nat. Rev. Microbiol. 9, 62–75 (2011).


    Google Scholar
     

  • Blondelle, S. E. & Houghten, R. A. Design of model amphipathic peptides having potent antimicrobial activities. Biochemistry 31, 12688–12694 (1992).


    Google Scholar
     

  • Zhang, S. K. et al. Design of an alpha-helical antimicrobial peptide with improved cell-selective and potent anti-biofilm activity. Sci. Rep. 6, 27394 (2016).


    Google Scholar
     

  • Silva, O. N. et al. Repurposing a peptide toxin from wasp venom into antiinfectives with dual antimicrobial and immunomodulatory properties. Proc. Natl. Acad. Sci. USA 117, 26936–26945 (2020).


    Google Scholar
     

  • Mwangi, J., Kamau, P. M., Thuku, R. C. & Lai, R. Design methods for antimicrobial peptides with improved performance. Zool. Res. 44, 1095–1114 (2023).


    Google Scholar
     

  • Lakshmaiah Narayana, J. et al. Two distinct amphipathic peptide antibiotics with systemic efficacy. Proc. Natl. Acad. Sci. USA 117, 19446–19454 (2020).


    Google Scholar
     

  • Wang, G. et al. Transformation of human cathelicidin LL-37 into selective, stable, and potent antimicrobial compounds. ACS Chem. Biol. 9, 1997–2002 (2014).


    Google Scholar
     

  • Mishra, B. et al. Sequence permutation generates peptides with different antimicrobial and antibiofilm activities. Pharmaceuticals 13, 271 (2020).

  • Rios, T. B. et al. Anti-staphy peptides rationally designed from Cry10Aa bacterial protein. ACS Omega 9, 29159–29174 (2024).


    Google Scholar
     

  • Brogden, K. A. Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria. Nat. Rev. Microbiol. 3, 238–250 (2005).


    Google Scholar
     

  • Lee, M. T., Chen, F. Y. & Huang, H. W. Energetics of pore formation induced by membrane active peptides. Biochemistry 43, 3590–3599 (2004).


    Google Scholar
     

  • Juba, M. L. et al. Helical cationic antimicrobial peptide length and its impact on membrane disruption. Biochim. Biophys. Acta 1848, 1081–1091 (2015).


    Google Scholar
     

  • Shi, J., Chen, C., Wang, D., Wang, Z. & Liu, Y. The antimicrobial peptide LI14 combats multidrug-resistant bacterial infections. Commun. Biol. 5, 926 (2022).


    Google Scholar
     

  • Ye, Z. et al. Synergistic collaboration between AMPs and non-direct antimicrobial cationic peptides. Nat. Commun. 15, 7319 (2024).


    Google Scholar
     

  • Gomari, M. M. et al. Peptidomimetics in cancer targeting. Mol. Med. 28, 146 (2022).


    Google Scholar
     

  • Mishra, B., Lakshmaiah Narayana, J., Lushnikova, T., Wang, X. & Wang, G. Low cationicity is important for systemic in vivo efficacy of database-derived peptides against drug-resistant Gram-positive pathogens. Proc. Natl. Acad. Sci. USA 116, 13517–13522 (2019).


    Google Scholar
     

  • Goormaghtigh, F. & Van Melderen, L. Single-cell imaging and characterization of Escherichia coli persister cells to ofloxacin in exponential cultures. Sci. Adv. 5, eaav9462 (2019).


    Google Scholar
     

  • Truong-Bolduc, Q. C. et al. Staphylococcus aureus AbcA transporter enhances persister formation under beta-lactam exposure. Antimicrob. Agents Chemother. 68, e0134023 (2024).


    Google Scholar
     

  • Thomas, R. E. & Thomas, B. C. Reducing biofilm infections in burn patients’ wounds and biofilms on surfaces in hospitals, medical facilities and medical equipment to improve burn care: a systematic review. Int. J. Environ. Res. Public Health 18, 13195 (2021).

  • Claeys, K. C. et al. Acute bacterial skin and skin structure infections treated with intravenous antibiotics in the emergency department or observational unit: experience at the detroit medical center. Infect. Dis. Ther. 4, 173–186 (2015).


    Google Scholar
     

  • Branski, L. K. et al. Emerging infections in burns. Surg. Infect. 10, 389–397 (2009).


    Google Scholar
     

  • Koziel, J. & Potempa, J. Protease-armed bacteria in the skin. Cell Tissue Res. 351, 325–337 (2013).


    Google Scholar
     

  • Wang, C. et al. Evolution of resistance mechanisms and biological characteristics of rifampicin-resistant Staphylococcus aureus strains selected in vitro. BMC Microbiol. 19, 220 (2019).


    Google Scholar
     

  • Zhu, X. et al. Nigericin is effective against multidrug resistant gram-positive bacteria, persisters, and biofilms. Front. Cell Infect. Microbiol. 12, 1055929 (2022).


    Google Scholar
     

  • Pirtskhalava, M. et al. DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res. 49, D288–D297 (2021).


    Google Scholar
     

  • Determination of minimum inhibitory concentrations (MICs) of antibacterial agents by broth dilution. Clin. Microbiol. Infect. 9, ix-xv (2003).

  • Landrum, G. RDKit: Open-source Cheminformatics https://www.rdkit.org (2023).

  • Cock, P. J. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).


    Google Scholar
     

  • Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702 e613 (2020).


    Google Scholar
     

  • Hall, L. H. & Kier, L. B. in Reviews in Computational Chemistry Reviews in Computational Chemistry, Vol. 21, 367–422 (Wiley, 1991).

  • Morgan, H. L. The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. J. Chem. Doc. 5, 107–113 (2002).


    Google Scholar
     

  • Elnaggar, A. et al. ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112–7127 (2022).


    Google Scholar
     

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).


    Google Scholar
     

  • Chen, T. & Guestrin, C. in Proc 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).

  • Kim, S.-J., Koh, K., Lustig, M., Boyd, S. & Gorinevsky, D. An interior-point method for large-scale-regularized least squares. IEEE J. Sel. Top. Signal Process. 1, 606–617 (2007).


    Google Scholar
     

  • Klaproth-Andrade, D. et al. Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing. Nat. Commun. 15, 151 (2024).


    Google Scholar
     

  • Rücker, C., Rücker, G. & Meringer, M. y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Modeling 47, 2345–2357 (2007).


    Google Scholar
     

  • Dathe, M. & Wieprecht, T. Structural features of helical antimicrobial peptides: their potential to modulate activity on model membranes and biological cells. Biochim. Biophys. Acta 1462, 71–87 (1999).


    Google Scholar
     

  • Li, J., Hu, S., Jian, W., Xie, C. & Yang, X. Plant antimicrobial peptides: structures, functions, and applications. Bot. Stud. 62, 5 (2021).


    Google Scholar
     

  • Wang, G., Li, X. & Wang, Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 44, D1087–D1093 (2016).


    Google Scholar
     

  • de Santana, C. J. C., Pires Junior, O. R., Fontes, W., Palma, M. S. & Castro, M. S. Mastoparans: a group of multifunctional alpha-helical peptides with promising therapeutic properties. Front. Mol. Biosci. 9, 824989 (2022).


    Google Scholar
     

  • Hori, Y. et al. Interaction of mastoparan with membranes studied by 1H-NMR spectroscopy in detergent micelles and by solid-state 2H-NMR and 15N-NMR spectroscopy in oriented lipid bilayers. Eur. J. Biochem. 268, 302–309 (2001).


    Google Scholar
     

  • Sahu, C., Jain, V., Mishra, P. & Prasad, K. N. Clinical and laboratory standards institute versus European Committee for antimicrobial susceptibility testing guidelines for interpretation of carbapenem antimicrobial susceptibility results for Escherichia coli in urinary tract infection (UTI). J. Lab Physicians 10, 289–293 (2018).


    Google Scholar
     

  • Felix, L., Mishra, B., Khader, R., Ganesan, N. & Mylonakis, E. In vitro and in vivo bactericidal and antibiofilm efficacy of alpha mangostin against Staphylococcus aureus persister cells. Front. Cell Infect. Microbiol. 12, 898794 (2022).


    Google Scholar
     

  • Kim, W. et al. Identification of an antimicrobial agent effective against methicillin-resistant Staphylococcus aureus persisters using a fluorescence-based screening strategy. PLoS One 10, e0127640 (2015).


    Google Scholar
     

  • Mishra, B. et al. Design and evaluation of short bovine lactoferrin-derived antimicrobial peptides against multidrug-resistant Enterococcus faecium. Antibiotics 11, 1085 (2022).

  • Peng, J., Mishra, B., Khader, R., Felix, L. & Mylonakis, E. Novel cecropin-4 derived peptides against methicillin-resistant Staphylococcus aureus. Antibiotics 10, 36 (2021).

  • Mishra, B. et al. Site specific immobilization of a potent antimicrobial peptide onto silicone catheters: evaluation against urinary tract infection pathogens. J. Mater. Chem. B 2, 1706–1716 (2014).


    Google Scholar
     

  • Sani, M. A., Rajput, S., Keizer, D. W. & Separovic, F. NMR techniques for investigating antimicrobial peptides in model membranes and bacterial cells. Methods 224, 10–20 (2024).


    Google Scholar
     

  • Crusca, E. Jr. et al. NMR structures and molecular dynamics simulation of hylin-a1 peptide analogs interacting with micelles. J. Pept. Sci. 23, 421–430 (2017).


    Google Scholar
     

  • Shen, Y., Delaglio, F., Cornilescu, G. & Bax, A. TALOS + : a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J. Biomol. NMR 44, 213–223 (2009).


    Google Scholar
     

  • Guntert, P. Automated NMR structure calculation with CYANA. Methods Mol. Biol. 278, 353–378 (2004).


    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).


    Google Scholar
     

  • Kumar, A., Mishra, B., Konar, A. D., Mylonakis, E. & Basu, A. Molecular dynamics simulations help determine the molecular mechanisms of lasioglossin-III and its variant peptides’ membrane interfacial interactions. J. Phys. Chem. B 128, 6049–6058 (2024).


    Google Scholar
     

  • Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).


    Google Scholar
     

  • Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph 14, 27–38 (1996).


    Google Scholar
     

  • Epand, R. M. & Epand, R. F. Lipid domains in bacterial membranes and the action of antimicrobial agents. Biochim. Biophys. Acta 1788, 289–294 (2009).


    Google Scholar
     

  • Kim, S. M. et al. Antimicrobial activity of the membrane-active compound nTZDpa is enhanced at low pH. Biomed. Pharmacother. 150, 112977 (2022).


    Google Scholar
     

  • Mishra, B. et al. A novel antimicrobial peptide derived from modified N-terminal domain of bovine lactoferrin: design, synthesis, activity against multidrug-resistant bacteria and Candida. Biochim. Biophys. Acta 1828, 677–686 (2013).


    Google Scholar
     

  • Sherman, M. B. et al. Near-atomic-resolution cryo-electron microscopy structures of cucumber leaf spot virus and red clover necrotic mosaic virus: evolutionary divergence at the icosahedral three-fold axes. J. Virol. 94, e01439-19 (2020).

  • Fey, P. D. et al. A genetic resource for rapid and comprehensive phenotype screening of nonessential Staphylococcus aureus genes. mBio 4, e00537–00512 (2013).


    Google Scholar
     

  • Novogene. Sequencing Platform https://www.novogene.com/us-en/technology/platforms/ (2025).

  • Oscorbin, I. P. & Filipenko, M. L. M-MuLV reverse transcriptase: Selected properties and improved mutants. Comput. Struct. Biotechnol. J. 19, 6315–6327 (2021).


    Google Scholar
     

  • Ling, L. L. et al. A new antibiotic kills pathogens without detectable resistance. Nature 517, 455–459 (2015).


    Google Scholar
     

  • McDonald, P. J., Craig, W. A. & Kunin, C. M. Persistent effect of antibiotics on Staphylococcus aureus after exposure for limited periods of time. J. Infect. Dis. 135, 217–223 (1977).


    Google Scholar
     

  • Haukland, H. H. & Vorland, L. H. Post-antibiotic effect of the antimicrobial peptide lactoferricin on Escherichia coli and Staphylococcus aureus. J. Antimicrob. Chemother. 48, 569–571 (2001).


    Google Scholar
     

  • de Breij, A. et al. The antimicrobial peptide SAAP-148 combats drug-resistant bacteria and biofilms. Sci. Transl. Med. 10, eaan4044 (2018).

  • The Cytokine Core. Sample Handling: Collection, Storage, Shipping, and Submission https://www.multiplex-cytokine-analysis.com/sample-collection-storage-shipping-and-submission/ (2025).

  • Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).


    Google Scholar
     



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