1932

Abstract

The rise of antibiotic-resistant strains of bacterial pathogens has necessitated the development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds with potentially attractive therapeutic properties, including the ability to target specific groups of bacteria. In nature, AMPs exhibit remarkable structural and functional diversity, which may be further enhanced through genetic engineering, high-throughput screening, and chemical modification strategies. In this review, we discuss the molecular mechanisms underlying AMP selectivity and highlight recent computational and experimental efforts to design selectively targeting AMPs. While there has been an extensive effort to find broadly active and highly potent AMPs, it remains challenging to design targeting peptides to discriminate between different bacteria on the basis of physicochemical properties. We also review approaches for measuring AMP activity, point out the challenges faced in assaying for selectivity, and discuss the potential for increasing AMP diversity through chemical modifications.

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2021-07-13
2024-04-16
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Literature Cited

  1. 1. 
    Mookherjee N, Anderson MA, Haagsman HP, Davidson DJ. 2020. Antimicrobial host defence peptides: functions and clinical potential. Nat. Rev. Drug Discov. 19:311–32
    [Google Scholar]
  2. 2. 
    Cotter PD, Ross RP, Hill C. 2013. Bacteriocins—a viable alternative to antibiotics?. Nat. Rev. Microbiol. 11:95–105
    [Google Scholar]
  3. 3. 
    Selber-Hnatiw S, Rukundo B, Ahmadi M, Akoubi H, Al-Bizri H et al. 2017. Human gut microbiota: toward an ecology of disease. Front. Microbiol. 8:1265
    [Google Scholar]
  4. 4. 
    Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E. 2017. Dysbiosis and the immune system. Nat. Rev. Immunol. 17:219–32
    [Google Scholar]
  5. 5. 
    Arrieta MC, Stiemsma LT, Amenyogbe N, Brown EM, Finlay B. 2014. The intestinal microbiome in early life: health and disease. Front. Immunol. 5:427
    [Google Scholar]
  6. 6. 
    Batoni G, Maisetta G, Esin S. 2016. Antimicrobial peptides and their interaction with biofilms of medically relevant bacteria. Biochim. Biophys. Acta Biomembr. 1858:1044–60
    [Google Scholar]
  7. 7. 
    Ostaff MJ, Stange EF, Wehkamp J. 2013. Antimicrobial peptides and gut microbiota in homeostasis and pathology. EMBO Mol. Med. 5:1465–83
    [Google Scholar]
  8. 8. 
    Garcia-Gutierrez E, Mayer MJ, Cotter PD, Narbad A. 2019. Gut microbiota as a source of novel antimicrobials. Gut Microbes 10:1–21
    [Google Scholar]
  9. 9. 
    de la Fuente-Nunez C, Torres MD, Mojica FJ, Lu TK. 2017. Next-generation precision antimicrobials: towards personalized treatment of infectious diseases. Curr. Opin. Microbiol. 37:95–102
    [Google Scholar]
  10. 10. 
    Dias C, Rauter AP. 2019. Membrane-targeting antibiotics: recent developments outside the peptide space. Future Med. Chem. 11:3211–28
    [Google Scholar]
  11. 11. 
    Mahlapuu M, Hakansson J, Ringstad L, Bjorn C. 2016. Antimicrobial peptides: an emerging category of therapeutic agents. Front. Cell. Infect. Microbiol. 6:194
    [Google Scholar]
  12. 12. 
    Malanovic N, Lohner K. 2016. Antimicrobial peptides targeting Gram-positive bacteria. Pharmaceuticals 9:359
    [Google Scholar]
  13. 13. 
    Epand RM, Epand RF. 2011. Bacterial membrane lipids in the action of antimicrobial agents. J. Pept. Sci. 17:298–305
    [Google Scholar]
  14. 14. 
    Hollmann A, Martinez M, Maturana P, Semorile LC, Maffia PC. 2018. Antimicrobial peptides: interaction with model and biological membranes and synergism with chemical antibiotics. Front. Chem. 6:204
    [Google Scholar]
  15. 15. 
    Munch D, Sahl HG. 2015. Structural variations of the cell wall precursor lipid II in Gram-positive bacteria—impact on binding and efficacy of antimicrobial peptides. Biochim. Biophys. Acta Biomembr. 1848:3062–71
    [Google Scholar]
  16. 16. 
    Scherer KM, Spille JH, Sahl HG, Grein F, Kubitscheck U. 2015. The lantibiotic nisin induces lipid II aggregation, causing membrane instability and vesicle budding. Biophys. J. 108:1114–24
    [Google Scholar]
  17. 17. 
    Mylonakis E, Podsiadlowski L, Muhammed M, Vilcinskas A. 2016. Diversity, evolution and medical applications of insect antimicrobial peptides. Philos. Trans. R. Soc. B 371:20150290
    [Google Scholar]
  18. 18. 
    Park SI, Kim JW, Yoe SM. 2015. Purification and characterization of a novel antibacterial peptide from black soldier fly (Hermetia illucens) larvae. Dev. Comp. Immunol. 52:98–106
    [Google Scholar]
  19. 19. 
    Otvos L Jr. 2002. The short proline-rich antibacterial peptide family. Cell. Mol. Life Sci. 59:1138–50
    [Google Scholar]
  20. 20. 
    Le C-F, Fang C-M, Sekaran SD. 2017. Intracellular targeting mechanisms by antimicrobial peptides. Antimicrob. Agents Chemother. 61:4e02340–16
    [Google Scholar]
  21. 21. 
    Hale JDF, Hancock REW. 2007. Alternative mechanisms of action of cationic antimicrobial peptides on bacteria. Expert Rev. Anti-Infect. Ther. 5:951–59
    [Google Scholar]
  22. 22. 
    Torres MDT, Sothiselvam S, Lu TK, de la Fuente-Nunez C. 2019. Peptide design principles for antimicrobial applications. J. Mol. Biol. 431:3547–67
    [Google Scholar]
  23. 23. 
    Guo L, McLean JS, Yang Y, Eckert R, Kaplan CW et al. 2015. Precision-guided antimicrobial peptide as a targeted modulator of human microbial ecology. PNAS 112:7569–74
    [Google Scholar]
  24. 24. 
    Kaplan CW, Sim JH, Shah KR, Kolesnikova-Kaplan A, Shi W, Eckert R. 2011. Selective membrane disruption: mode of action of C16G2, a specifically targeted antimicrobial peptide. Antimicrob. Agents Chemother. 55:3446–52
    [Google Scholar]
  25. 25. 
    Xu L, Shao C, Li G, Shan A, Chou S et al. 2020. Conversion of broad-spectrum antimicrobial peptides into species-specific antimicrobials capable of precisely targeting pathogenic bacteria. Sci. Rep. 10:944
    [Google Scholar]
  26. 26. 
    Choudhury A, Islam SMA, Ghidey MR, Kearney CM. 2020. Repurposing a drug targeting peptide for targeting antimicrobial peptides against Staphylococcus. Biotechnol. Lett. 42:287–94
    [Google Scholar]
  27. 27. 
    Zhu X, Ma Z, Wang J, Chou S, Shan A. 2014. Importance of tryptophan in transforming an amphipathic peptide into a Pseudomonas aeruginosa-targeted antimicrobial peptide. PLOS ONE 9:e114605
    [Google Scholar]
  28. 28. 
    Edwards IA, Elliott AG, Kavanagh AM, Zuegg J, Blaskovich MA, Cooper MA. 2016. Contribution of amphipathicity and hydrophobicity to the antimicrobial activity and cytotoxicity of β-hairpin peptides. ACS Infect. Dis. 2:442–50
    [Google Scholar]
  29. 29. 
    Postma TM, Liskamp RMJ. 2016. Triple-targeting Gram-negative selective antimicrobial peptides capable of disrupting the cell membrane and lipid A biosynthesis. RSC Adv 6:65418–21
    [Google Scholar]
  30. 30. 
    Muhle SA, Tam JP. 2001. Design of Gram-negative selective antimicrobial peptides. Biochemistry 40:5777–85
    [Google Scholar]
  31. 31. 
    Idso MN, Akhade AS, Arrieta-Ortiz ML, Lai BT, Srinivas V et al. 2020. Antibody-recruiting protein-catalyzed capture agents to combat antibiotic-resistant bacteria. Chem. Sci. 11:3054–67
    [Google Scholar]
  32. 32. 
    Veltri D, Kamath U, Shehu A. 2018. Deep learning improves antimicrobial peptide recognition. Bioinformatics 34:2740–47
    [Google Scholar]
  33. 33. 
    Lee EY, Fulan BM, Wong GC, Ferguson AL 2016. Mapping membrane activity in undiscovered peptide sequence space using machine learning. PNAS 113:13588–93
    [Google Scholar]
  34. 34. 
    Yoshida M, Hinkley T, Tsuda S, Abul-Haija YM, McBurney RT et al. 2018. Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides. Chemistry 4:533–43
    [Google Scholar]
  35. 35. 
    Porto WF, Irazazabal L, Alves ESF, Ribeiro SM, Matos CO et al. 2018. In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design. Nat. Commun. 9:1490
    [Google Scholar]
  36. 36. 
    Gabere MN, Noble WS. 2017. Empirical comparison of web-based antimicrobial peptide prediction tools. Bioinformatics 33:1921–29
    [Google Scholar]
  37. 37. 
    Spanig S, Heider D. 2019. Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Min 12:7
    [Google Scholar]
  38. 38. 
    Vishnepolsky B, Zaalishvili G, Karapetian M, Nasrashvili T, Kuljanishvili N et al. 2019. De novo design and in vitro testing of antimicrobial peptides against gram-negative bacteria. Pharmaceuticals 12:82
    [Google Scholar]
  39. 39. 
    Nagarajan D, Nagarajan T, Roy N, Kulkarni O, Ravichandran S et al. 2018. Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria. J. Biol. Chem. 293:3492–509
    [Google Scholar]
  40. 40. 
    Veltri D, Kamath U, Shehu A. 2017. Improving recognition of antimicrobial peptides and target selectivity through machine learning and genetic programming. IEEE/ACM Trans. Comput. Biol. Bioinform. 14:300–13
    [Google Scholar]
  41. 41. 
    Majumder A, Biswal MR, Prakash MK. 2019. Computational screening of antimicrobial peptides for Acinetobacter baumannii. PLOS ONE 14:e0219693
    [Google Scholar]
  42. 42. 
    Maccari G, Di Luca M, Nifosi R, Cardarelli F, Signore G et al. 2013. Antimicrobial peptides design by evolutionary multiobjective optimization. PLOS Comput. Biol. 9:e1003212
    [Google Scholar]
  43. 43. 
    Gull S, Minhas F. 2020. AMP0: species-specific prediction of anti-microbial peptides using zero and few shot learning. IEEE/ACM Trans. Comput. Biol. Bioinform. In press. https://doi.org/10.1109/TCBB.2020.2999399
    [Crossref] [Google Scholar]
  44. 44. 
    Rea MC, Sit CS, Clayton E, O'Connor PM, Whittal RM et al. 2010. Thuricin CD, a posttranslationally modified bacteriocin with a narrow spectrum of activity against Clostridium difficile. PNAS 107:9352–57
    [Google Scholar]
  45. 45. 
    Lopez-Perez PM, Grimsey E, Bourne L, Mikut R, Hilpert K. 2017. Screening and optimizing antimicrobial peptides by using SPOT-synthesis. Front. Chem. 5:25
    [Google Scholar]
  46. 46. 
    Mijalis AJ, Thomas DA 3rd, Simon MD, Adamo A, Beaumont R et al. 2017. A fully automated flow-based approach for accelerated peptide synthesis. Nat. Chem. Biol. 13:464–66
    [Google Scholar]
  47. 47. 
    Albin JS, Pentelute BL. 2020. Efficient flow synthesis of human antimicrobial peptides. Aust. J. Chem. 73:380–88
    [Google Scholar]
  48. 48. 
    Ritter SC, Yang ML, Kaznessis YN, Hackel BJ. 2018. Multispecies activity screening of microcin J25 mutants yields antimicrobials with increased specificity toward pathogenic Salmonella species relative to human commensal Escherichia coli. Biotechnol. Bioeng. 115:2394–404
    [Google Scholar]
  49. 49. 
    Tucker AT, Leonard SP, DuBois CD, Knauf GA, Cunningham AL et al. 2018. Discovery of next-generation antimicrobials through bacterial self-screening of surface-displayed peptide libraries. Cell 172:618–28.e13
    [Google Scholar]
  50. 50. 
    McCarthy KA, Kelly MA, Li K, Cambray S, Hosseini AS et al. 2018. Phage display of dynamic covalent binding motifs enables facile development of targeted antibiotics. J. Am. Chem. Soc. 140:6137–45
    [Google Scholar]
  51. 51. 
    Scanlon TC, Dostal SM, Griswold KE. 2014. A high-throughput screen for antibiotic drug discovery. Biotechnol. Bioeng. 111:232–43
    [Google Scholar]
  52. 52. 
    Rea MC, Clayton E, O'Connor PM, Shanahan F, Kiely B et al. 2007. Antimicrobial activity of lacticin 3147 against clinical Clostridium difficile strains. J. Med. Microbiol. 56:940–46
    [Google Scholar]
  53. 53. 
    Balouiri M, Sadiki M, Ibnsouda SK. 2016. Methods for in vitro evaluating antimicrobial activity: a review. J. Pharm. Anal. 6:71–79
    [Google Scholar]
  54. 54. 
    Wiegand I, Hilpert K, Hancock RE. 2008. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 3:163–75
    [Google Scholar]
  55. 55. 
    Mercer DK, Torres MDT, Duay SS, Lovie E, Simpson L et al. 2020. Antimicrobial susceptibility testing of antimicrobial peptides to better predict efficacy. Front. Cell. Infect. Microbiol. 10:326
    [Google Scholar]
  56. 56. 
    Auchtung JM, Robinson CD, Britton RA. 2015. Cultivation of stable, reproducible microbial communities from different fecal donors using minibioreactor arrays (MBRAs). Microbiome 3:42
    [Google Scholar]
  57. 57. 
    Watterson WJ, Tanyeri M, Watson AR, Cham CM, Shan Y et al. 2020. Droplet-based high-throughput cultivation for accurate screening of antibiotic resistant gut microbes. eLife 9:e56998
    [Google Scholar]
  58. 58. 
    Vasilchenko AS, Rogozhin EA. 2019. Sub-inhibitory effects of antimicrobial peptides. Front. Microbiol. 10:1160
    [Google Scholar]
  59. 59. 
    Adamowicz EM, Flynn J, Hunter RC, Harcombe WR. 2018. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J 12:2723–35
    [Google Scholar]
  60. 60. 
    Kim H, Jang JH, Kim SC, Cho JH. 2020. Development of a novel hybrid antimicrobial peptide for targeted killing of Pseudomonas aeruginosa. Eur. J. Med. Chem. 185:111814
    [Google Scholar]
  61. 61. 
    Sun Z, Zhong J, Liang X, Liu J, Chen X, Huan L 2009. Novel mechanism for nisin resistance via proteolytic degradation of nisin by the nisin resistance protein NSR. Antimicrob. Agents Chemother. 53:1964–73
    [Google Scholar]
  62. 62. 
    Browne K, Chakraborty S, Chen R, Willcox MD, Black DS et al. 2020. A new era of antibiotics: the clinical potential of antimicrobial peptides. Int. J. Mol. Sci. 21:7047
    [Google Scholar]
  63. 63. 
    Ebbensgaard A, Mordhorst H, Overgaard MT, Nielsen CG, Aarestrup FM, Hansen EB. 2015. Comparative evaluation of the antimicrobial activity of different antimicrobial peptides against a range of pathogenic bacteria. PLOS ONE 10:e0144611
    [Google Scholar]
  64. 64. 
    Starr CG, Wimley WC. 2017. Antimicrobial peptides are degraded by the cytosolic proteases of human erythrocytes. Biochim. Biophys. Acta Biomembr. 1859:2319–26
    [Google Scholar]
  65. 65. 
    Starr CG, Ghimire J, Guha S, Hoffmann JP, Wang Y et al. 2020. Synthetic molecular evolution of host cell-compatible, antimicrobial peptides effective against drug-resistant, biofilm-forming bacteria. PNAS 117:8437–48
    [Google Scholar]
  66. 66. 
    Koehbach J, Craik DJ. 2019. The vast structural diversity of antimicrobial peptides. Trends Pharmacol. Sci. 40:517–28
    [Google Scholar]
  67. 67. 
    Wang G. 2012. Post-translational modifications of natural antimicrobial peptides and strategies for peptide engineering. Curr. Biotechnol. 1:72–79
    [Google Scholar]
  68. 68. 
    Chin JW. 2017. Expanding and reprogramming the genetic code. Nature 550:53–60
    [Google Scholar]
  69. 69. 
    Baumann T, Nickling JH, Bartholomae M, Buivydas A, Kuipers OP, Budisa N. 2017. Prospects of in vivo incorporation of non-canonical amino acids for the chemical diversification of antimicrobial peptides. Front. Microbiol. 8:124
    [Google Scholar]
  70. 70. 
    Li FF, Brimble MA. 2019. Using chemical synthesis to optimise antimicrobial peptides in the fight against antimicrobial resistance. Pure Appl. Chem. 91:181–98
    [Google Scholar]
  71. 71. 
    Hicks RP, Bhonsle JB, Venugopal D, Koser BW, Magill AJ. 2007. De novo design of selective antibiotic peptides by incorporation of unnatural amino acids. J. Med. Chem. 50:3026–36
    [Google Scholar]
  72. 72. 
    Piscotta FJ, Tharp JM, Liu WR, Link AJ. 2015. Expanding the chemical diversity of lasso peptide MccJ25 with genetically encoded noncanonical amino acids. Chem. Commun. 51:409–12
    [Google Scholar]
  73. 73. 
    Bartholomae M, Baumann T, Nickling JH, Peterhoff D, Wagner R et al. 2018. Expanding the genetic code of Lactococcus lactis and Escherichia coli to incorporate non-canonical amino acids for production of modified lantibiotics. Front. Microbiol. 9:657
    [Google Scholar]
  74. 74. 
    Rezhdo A, Islam M, Huang M, Van Deventer JA. 2019. Future prospects for noncanonical amino acids in biological therapeutics. Curr. Opin. Biotechnol. 60:168–78
    [Google Scholar]
  75. 75. 
    Reinhardt A, Neundorf I. 2016. Design and application of antimicrobial peptide conjugates. Int. J. Mol. Sci. 17:5701
    [Google Scholar]
  76. 76. 
    Touti F, Lautrette G, Johnson KD, Delaney JC, Wollacott A et al. 2018. Antibody-bactericidal macrocyclic peptide conjugates to target Gram-negative bacteria. ChemBioChem 19:2039–44
    [Google Scholar]
  77. 77. 
    Mondhe M, Chessher A, Goh S, Good L, Stach JE. 2014. Species-selective killing of bacteria by antimicrobial peptide-PNAs. PLOS ONE 9:e89082
    [Google Scholar]
  78. 78. 
    Blaskovich MAT, Hansford KA, Gong Y, Butler MS, Muldoon C et al. 2018. Protein-inspired antibiotics active against vancomycin- and daptomycin-resistant bacteria. Nat. Commun. 9:22
    [Google Scholar]
  79. 79. 
    Mishra NM, Briers Y, Lamberigts C, Steenackers H, Robijns S et al. 2015. Evaluation of the antibacterial and antibiofilm activities of novel CRAMP-vancomycin conjugates with diverse linkers. Org. Biomol. Chem. 13:7477–86
    [Google Scholar]
  80. 80. 
    Defraine V, Schuermans J, Grymonprez B, Govers SK, Aertsen A et al. 2016. Efficacy of Artilysin Art-175 against resistant and persistent Acinetobacter baumannii. Antimicrob. Agents Chemother. 60:3480–88
    [Google Scholar]
  81. 81. 
    Tan P, Lai ZH, Zhu YJ, Shao CX, Akhtar MU et al. 2020. Multiple strategy optimization of specifically targeted antimicrobial peptide based on structure–activity relationships to enhance bactericidal efficiency. ACS Biomater. Sci. Eng. 6:398–414
    [Google Scholar]
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