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2000
Volume 25, Issue 5
  • ISSN: 1566-5232
  • E-ISSN: 1875-5631

Abstract

Introduction

Antimicrobial peptides (AMPs), unlike antibiotics, are encoded in genomes. AMPs are exported from the cell after expression and translation. In the case of bacteria, the exported peptides target other microbes to give the producing bacterium a competitive edge. While AMPs are sought after for their similar antimicrobial activity to traditional antibiotics, it is difficult to predict which combinations of amino acids will confer antimicrobial activity. Many computer algorithms have been designed to predict whether a sequence of amino acids will exhibit antimicrobial activity, but the vast majority of validated AMPs in databases are still of eukaryotic origin. This defies common sense since the vast majority of life on Earth is prokaryotic.

Methods

The antimicrobial peptide pipeline, presented here, is a bacteria-centric AMP predictor that predicts AMPs by taking design inspiration from the sequence properties of bacterial genomes with the intention to improve the detection of naturally occurring bacterial AMPs. The pipeline integrates multiple concepts of comparative biology to search for candidate AMPs at the primary, secondary, and tertiary peptide structure levels.

Results

Results showed that the antimicrobial peptide pipeline identifies known AMPs that are missed by state-of-the-art AMP predictors and that the pipeline yields more AMP candidates from real bacterial genomes than from fake genomes, with the rate of AMP detection being significantly higher in the genomes of six nosocomial pathogens than in the fake genomes.

Conclusion

This bacteria-centric AMP pipeline enhances the detection of bacterial AMPs by incorporating sequence properties unique to bacterial genomes. It complements existing tools, addressing gaps in AMP detection and providing a promising avenue for discovering novel antimicrobial peptides.

© 2025 Bentham Science Publishers
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References

  1. LiS. WangY. XueZ. The structure-mechanism relationship and mode of actions of antimicrobial peptides: A review.Trends Food Sci. Technol.202110910311510.1016/j.tifs.2021.01.005
     [Google Scholar]
  2. WangG. LiX. WangZ. APD3: The antimicrobial peptide database as a tool for research and education.Nucleic Acids Res.201644D1D1087D109310.1093/nar/gkv1278 26602694
     [Google Scholar]
  3. ChenN. JiangC. Antimicrobial peptides: Structure, mechanism, and modification.Eur. J. Med. Chem.202325511537710.1016/j.ejmech.2023.115377 37099837
     [Google Scholar]
  4. XuJ. LiF. LeierA. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.Brief. Bioinform.2021225bbab08310.1093/bib/bbab083 33774670
     [Google Scholar]
  5. NtountoumiC. VlastaridisP. MossialosD. Low complexity regions in the proteins of prokaryotes perform important functional roles and are highly conserved.Nucleic Acids Res.2019471999981000910.1093/nar/gkz730 31504783
     [Google Scholar]
  6. XiongH. BuckwalterB.L. ShiehH.M. HechtM.H. Periodicity of polar and nonpolar amino acids is the major determinant of secondary structure in self-assembling oligomeric peptides.Proc. Natl. Acad. Sci. USA199592146349635310.1073/pnas.92.14.6349 7603994
     [Google Scholar]
  7. KabschW. SanderC. Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features.Biopolymers198322122577263710.1002/bip.360221211 6667333
     [Google Scholar]
  8. ErmolaevaM.D. Synonymous codon usage in bacteria.Curr. Issues Mol. Biol.2001349197 11719972
     [Google Scholar]
  9. SidorczukK. GagatP. PietluchF. Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data.Brief. Bioinform.2022235bbac34310.1093/bib/bbac343 35988923
     [Google Scholar]
  10. PortoW.F. FensterseiferI.C.M. RibeiroS.M. FrancoO.L. Joker: An algorithm to insert patterns into sequences for designing antimicrobial peptides.Biochim. Biophys. Acta, Gen. Subj.2018186292043205210.1016/j.bbagen.2018.06.011 29928920
     [Google Scholar]
  11. JumperJ. EvansR. PritzelA. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑2 34265844
     [Google Scholar]
  12. KempenvM KimSS TumescheitC Fast and accurate protein structure search with foldseek.Nat Biotechnol2024422243610.1038/s41587‑023‑01773‑037156916
     [Google Scholar]
  13. R CoreTeam R A Language and Environment for Statistical Computing 2024.Vienna, AustriaR Foundation for Statistical Computing2024
     [Google Scholar]
  14. RiceP. LongdenI. BleasbyA. EMBOSS: The european molecular biology open software suite.Available from: https://www.R-project.org/ Trends Genet2000166276710.1016/S0168‑9525(00)02024‑2 10827456
  15. VeldsmanW.P. Fasta_doctor: Software to fix a FASTA file.Zenodo2024Available from: https://bioinformatics.stackexchange.com/questions/19656/fixing-fasta-file-for-local-blast-database 10.5281/zenodo.12618130
     [Google Scholar]
  16. ShenW. LeS. LiY. HuF. SeqKit: A cross-platform and ultrafast toolkit for FASTA/Q file manipulation.PLoS One20161110e016396210.1371/journal.pone.0163962 27706213
     [Google Scholar]
  17. OsorioD. Rondón-VillarrealP. TorresR. Peptides: A package for data mining of antimicrobial peptides.R J.201571410.32614/RJ‑2015‑001
     [Google Scholar]
  18. JonesD.T. SwindellsM.B. Getting the most from PSI–BLAST.Trends Biochem. Sci.200227316116410.1016/S0968‑0004(01)02039‑4 11893514
     [Google Scholar]
  19. CordesM.H.J. DavidsonA.R. SauerR.T. Sequence space, folding and protein design.Curr. Opin. Struct. Biol.19966131010.1016/S0959‑440X(96)80088‑1 8696970
     [Google Scholar]
  20. KatohK. StandleyD.M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability.Mol. Biol. Evol.201330477278010.1093/molbev/mst010 23329690
     [Google Scholar]
  21. Capella-GutiérrezS. Silla-MartínezJ.M. GabaldónT. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses.Bioinformatics200925151972197310.1093/bioinformatics/btp348 19505945
     [Google Scholar]
  22. LinZ. AkinH. RaoR. Evolutionary-scale prediction of atomic-level protein structure with a language model.Science202337966371123113010.1126/science.ade2574 36927031
     [Google Scholar]
  23. TouwW.G. BaakmanC. BlackJ. A series of PDB-related databanks for everyday needs.Nucleic Acids Res.201543D1D364D36810.1093/nar/gku1028 25352545
     [Google Scholar]
  24. RaschkaS. MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack.J. Open Source Softw.201832463810.21105/joss.00638
     [Google Scholar]
  25. ChenT. GuestrinC. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining ACM201678579410.1145/2939672.2939785
     [Google Scholar]
  26. XiaoN. CaoD.S. ZhuM.F. XuQ.S. protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences.Bioinformatics201531111857185910.1093/bioinformatics/btv042 25619996
     [Google Scholar]
  27. BurdukiewiczM. SidorczukK. RafaczD. Proteomic screening for prediction and design of antimicrobial peptides with AmpGram.Int. J. Mol. Sci.20202112431010.3390/ijms21124310 32560350
     [Google Scholar]
  28. LawrenceT.J. CarperD.L. SpanglerM.K. AmPEPpy 1.0: A portable and accurate antimicrobial peptide prediction tool.Bioinformatics202137142058206010.1093/bioinformatics/btaa917 33135060
     [Google Scholar]
  29. Hernández-ElizárragaV.H. Ocharán-MercadoA. Olguín-LópezN. New insights into the toxin diversity and antimicrobial activity of the “Fire Coral” Millepora complanata.Toxins202214320610.3390/toxins14030206 35324703
     [Google Scholar]
  30. GagatP. Duda-MadejA. OstrówkaM. Testing antimicrobial properties of selected short amyloids.Int. J. Mol. Sci.202324180410.3390/ijms24010804 36614244
     [Google Scholar]
  31. OstrówkaM. Duda-MadejA. PietluchF. MackiewiczP. GagatP. Testing antimicrobial properties of human lactoferrin-derived fragments.Int. J. Mol. Sci.202324131052910.3390/ijms241310529 37445717
     [Google Scholar]
  32. CenaD.G.L. ScavassaB.V. ConceiçãoK. In silico prediction of anti-infective and cell-penetrating peptides from Thalassophryne nattereri natterin toxins.Pharmaceuticals2022159114110.3390/ph15091141 36145362
     [Google Scholar]
  33. SimonsA. AlhanoutK. DuvalR.E. Bacteriocins, antimicrobial peptides from bacterial origin: Overview of their biology and their impact against multidrug-resistant bacteria.Microorganisms20208563910.3390/microorganisms8050639 32349409
     [Google Scholar]
  34. LyY.T. LeukoS. MoellerR. An overview of the bacterial microbiome of public transportation systems—risks, detection, and countermeasures.Front. Public Health202412136732410.3389/fpubh.2024.1367324 38528857
     [Google Scholar]
  35. MahmoodH.Y. JamshidiS. SuttonJ.M. RahmanK.M. Current advances in developing inhibitors of bacterial multidrug efflux pumps.Curr. Med. Chem.201623101062108110.2174/0929867323666160304150522 26947776
     [Google Scholar]
  36. WanF. WongF. CollinsJ.J. de la Fuente-NunezC. Machine learning for antimicrobial peptide identification and design.Nat. Rev. Bioeng.20242539240710.1038/s44222‑024‑00152‑x
     [Google Scholar]
  37. LiC. SutherlandD. HammondS.A. AMPlify: Attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens.BMC Genomics20222317710.1186/s12864‑022‑08310‑4 35078402
     [Google Scholar]
  38. Santos-JúniorC.D. PanS. ZhaoX.M. CoelhoL.P. Macrel: Antimicrobial peptide screening in genomes and metagenomes.PeerJ20208e1055510.7717/peerj.10555 33384902
     [Google Scholar]
  39. RádaiZ. KissJ. NagyN.A. Taxonomic bias in AMP prediction of invertebrate peptides.Sci. Rep.20211111792410.1038/s41598‑021‑97415‑z 34504226
     [Google Scholar]
  40. BucataruC. CiobanasuC. Antimicrobial peptides: Opportunities and challenges in overcoming resistance.Microbiol. Res.202428612782210.1016/j.micres.2024.127822 38986182
     [Google Scholar]
/content/journals/cgt/10.2174/0115665232343790250120071445
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The Antimicrobial Peptide Pipeline: A Bacteria-Centric AMP Predictor
Curr. Gene Ther. 25, 786 (2025); https://doi.org/10.2174/0115665232343790250120071445
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  • Article Type:     Research Article
Keyword(s): AMPs; antibiotics; antimicrobial peptides; bacterial peptides; enzymatic metabolism; functional proteomics

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