Skip to main content
Log in

Expanding the Database of Signal-Anchor-Release Domain Endolysins Through Metagenomics

  • Published:
Probiotics and Antimicrobial Proteins Aims and scope Submit manuscript

Abstract

Endolysins are bacteriophage-derived lytic enzymes with antimicrobial activity. The action of endolysins against Gram-negative bacteria remains a challenge due to the physical protection of the outer membrane. However, recent research has demonstrated that signal-anchor-release (SAR) endolysins permeate the outer membrane of Gram-negative bacteria. This study investigates 2628 putative endolysin genes identified in 183,298 bacteriophage genomes. Previously, bioinformatic approaches resulted in a database of 66 SAR endolysins. This manuscript almost doubles the list with 53 additional SAR endolysin candidates. Forty-eight of the putative SAR endolysins described in this study contained one muramidase catalytic domain, and five included additional cell wall–binding domains at the C-terminus. For the moment, SAR domains are found in four protein families: glycoside hydrolase family 19 (GH19), glycoside hydrolase family 24 (GH24), glycoside hydrolase family 25 (GH25), and glycoside hydrolase family 108 (GH108). These SAR lysis are clustered in eight groups based on biochemical properties and domain presence/absence. Therefore, in this study, we expand the arsenal of endolysin candidates that might act against Gram-negative bacteria and develop a consult database for antimicrobial proteins derived from bacteriophages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Data Availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

References

  1. Chanishvili N (2012) Phage therapy–history from Twort and d’Herelle through Soviet experience to current approaches. Adv Virus Res 83:3–40. https://doi.org/10.1016/B978-0-12-394438-2.00001-3

    Article  CAS  PubMed  Google Scholar 

  2. Du Toit A (2017) The language of phages. Nat Rev Microbiol 15:135–135. https://doi.org/10.1038/nrmicro.2017.8

    Article  CAS  PubMed  Google Scholar 

  3. Chan BK, Abedon ST (2015) Bacteriophages and their enzymes in biofilm control. Curr Pharm Des 21:85–99. https://doi.org/10.2174/1381612820666140905112311

    Article  CAS  PubMed  Google Scholar 

  4. Lin DM, Koskella B, Lin HC (2017) Phage therapy: an alternative to antibiotics in the age of multi-drug resistance. World J Gastrointest Pharmacol Ther 8:162–173. https://doi.org/10.4292/wjgpt.v8.i3.162

    Article  PubMed  PubMed Central  Google Scholar 

  5. Principi N, Silvestri E, Esposito S (2019) Advantages and limitations of bacteriophages for the treatment of bacterial infections. Front Pharmacol. https://doi.org/10.3389/fphar.2019.00513

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hobbs Z, Abedon ST (2016) Diversity of phage infection types and associated terminology: the problem with ‘Lytic or lysogenic.’ FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnw047

    Article  PubMed  Google Scholar 

  7. Baliga P, Goolappa PT, Shekar M, Kallappa GS (2022) Cloning, characterization, and antibacterial properties of endolysin LysE against planktonic cells and biofilms of Aeromonas hydrophila. Probiotics Antimicrob Proteins. https://doi.org/10.1007/s12602-021-09880-7

    Article  PubMed  Google Scholar 

  8. Gontijo MTP, Vidigal PMP, Lopez MES, Brocchi M (2021) Bacteriophages that infect Gram-negative bacteria as source of signal-arrest-release motif lysins. Res Microbiol 172:103794. https://doi.org/10.1016/j.resmic.2020.103794

    Article  CAS  PubMed  Google Scholar 

  9. Young R (2014) Phage lysis: three steps, three choices, one outcome. J Microbiol 52:243–258. https://doi.org/10.1007/s12275-014-4087-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Xu M, Struck DK, Deaton J et al (2004) A signal-arrest-release sequence mediates export and control of the phage P1 endolysin. Proc Natl Acad Sci USA 101:6415–6420. https://doi.org/10.1073/pnas.0400957101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Gontijo MTP, Jorge GP, Brocchi M (2021) Current status of endolysin-based treatments against Gram-negative bacteria. Antibiotics 10:1143. https://doi.org/10.3390/antibiotics10101143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Briers Y, Schmelcher M, Loessner MJ et al (2009) The high-affinity peptidoglycan binding domain of Pseudomonas phage endolysin KZ144. Biochem Biophys Res Commun 383:187–191. https://doi.org/10.1016/j.bbrc.2009.03.161

    Article  CAS  PubMed  Google Scholar 

  13. Sekiya H, Kamitori S, Nariya H et al (2021) Structural and biochemical characterization of the Clostridium perfringens-specific Zn2+-dependent amidase endolysin, Psa, catalytic domain. Biochem Biophys Res Commun 576:66–72. https://doi.org/10.1016/j.bbrc.2021.08.085

    Article  CAS  PubMed  Google Scholar 

  14. Simmons M, Morales CA, Oakley BB, Seal BS (2012) Recombinant expression of a putative amidase cloned from the genome of Listeria monocytogenes that lyses the bacterium and its monolayer in conjunction with a protease. Probiotics & Antimicro Prot 4:1–10. https://doi.org/10.1007/s12602-011-9084-5

    Article  CAS  Google Scholar 

  15. Hosseini ES, Moniri R, Goli YD, Kashani HH (2016) Purification of antibacterial CHAPK protein using a self-cleaving fusion tag and its activity against methicillin-resistant Staphylococcus aureus. Probio  Antimicro Prot 8:202–210. https://doi.org/10.1007/s12602-016-9236-8

    Article  CAS  Google Scholar 

  16. Xu M, Arulandu A, Struck DK et al (2005) Disulfide isomerization after membrane release of its SAR domain activates P1 lysozyme. Science. https://doi.org/10.1126/science.1105143

    Article  PubMed  PubMed Central  Google Scholar 

  17. Park T, Struck DK, Dankenbring CA, Young R (2007) The pinholin of lambdoid phage 21: control of lysis by membrane depolarization. J Bacteriol 189:9135–9139. https://doi.org/10.1128/JB.00847-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tran TAT, Struck DK, Young R (2007) The T4 RI antiholin has an N-terminal signal anchor release domain that targets it for degradation by DegP. J Bacteriol. https://doi.org/10.1128/JB.00854-07

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lim J-A, Shin H, Heu S, Ryu S (2014) Exogenous lytic activity of SPN9CC endolysin against gram-negative bacteria. J Microbiol Biotechnol 24:803–811. https://doi.org/10.4014/jmb.1403.03035

    Article  CAS  PubMed  Google Scholar 

  20. Oliveira H, Melo LDR, Santos SB et al (2013) Molecular aspects and comparative genomics of bacteriophage endolysins. J Virol 87:4558–4570. https://doi.org/10.1128/JVI.03277-12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Valero-Rello A (2019) Diversity, specificity and molecular evolution of the lytic arsenal of Pseudomonas phages: in silico perspective. Environ Microbiol 21:4136–4150. https://doi.org/10.1111/1462-2920.14767

    Article  CAS  PubMed  Google Scholar 

  22. Fernández-Ruiz I, Coutinho FH, Rodriguez-Valera F (2018) Thousands of novel endolysins discovered in uncultured phage genomes. Front Microbiol. https://doi.org/10.3389/fmicb.2018.01033

    Article  PubMed  PubMed Central  Google Scholar 

  23. Mizuno CM, Rodriguez-Valera F, Kimes NE, Ghai R (2013) Expanding the marine virosphere using metagenomics. PLoS Genet 9:e1003987. https://doi.org/10.1371/journal.pgen.1003987

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mizuno CM, Ghai R, Saghaï A et al (2016) Genomes of abundant and widespread viruses from the deep ocean. mBio 7. https://doi.org/10.1128/mBio.00805-16

  25. Roux S, Brum JR, Dutilh BE et al (2016) Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537:689–693. https://doi.org/10.1038/nature19366

    Article  CAS  PubMed  Google Scholar 

  26. Coutinho FH, Silveira CB, Gregoracci GB et al (2017) Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun 8:15955. https://doi.org/10.1038/ncomms15955

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA et al (2016) Uncovering Earth’s virome. Nature 536:425–430. https://doi.org/10.1038/nature19094

    Article  CAS  PubMed  Google Scholar 

  28. Roux S, Hallam SJ, Woyke T, Sullivan MB (2015) Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 4:e08490. https://doi.org/10.7554/eLife.08490

  29. Hirokawa T, Boon-Chieng S, Mitaku S (1998) SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 14:378–379. https://doi.org/10.1093/bioinformatics/14.4.378

    Article  CAS  PubMed  Google Scholar 

  30. Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580. https://doi.org/10.1006/jmbi.2000.4315

    Article  CAS  PubMed  Google Scholar 

  31. Käll L, Krogh A, Sonnhammer ELL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036. https://doi.org/10.1016/j.jmb.2004.03.016

    Article  CAS  PubMed  Google Scholar 

  32. Hennerdal A, Elofsson A (2011) Rapid membrane protein topology prediction. Bioinformatics 27:1322–1323. https://doi.org/10.1093/bioinformatics/btr119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kozlowski LP (2021) IPC 2.0: prediction of isoelectric point and pKa dissociation constants. Nucleic Acids Res 49:W285–W292. https://doi.org/10.1093/nar/gkab295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zimmermann L, Stephens A, Nam S-Z et al (2018) A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J Mol Biol 430:2237–2243. https://doi.org/10.1016/j.jmb.2017.12.007

    Article  CAS  PubMed  Google Scholar 

  35. Mistry J, Chuguransky S, Williams L et al (2021) Pfam: the protein families database in 2021. Nucleic Acids Res 49:D412–D419. https://doi.org/10.1093/nar/gkaa913

    Article  CAS  PubMed  Google Scholar 

  36. Katoh K, Rozewicki J, Yamada KD (2019) MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform 20:1160–1166. https://doi.org/10.1093/bib/bbx108

    Article  CAS  PubMed  Google Scholar 

  37. Crooks GE, Hon G, Chandonia J-M, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190. https://doi.org/10.1101/gr.849004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Metsalu T, Vilo J (2015) ClustVis: a web tool for visualizing clustering of multivariate data using principal component analysis and heatmap. Nucleic Acids Res 43:W566–W570. https://doi.org/10.1093/nar/gkv468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Han MV, Zmasek CM (2009) phyloXML: XML for evolutionary biology and comparative genomics. BMC Bioinformatics 10:356. https://doi.org/10.1186/1471-2105-10-356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Takahashi D, Fujiwara I, Miyata M (2020) Phylogenetic origin and sequence features of MreB from the wall-less swimming bacteria Spiroplasma. Biochem Biophys Res Commun 533:638–644. https://doi.org/10.1016/j.bbrc.2020.09.060

    Article  CAS  PubMed  Google Scholar 

  41. Baek M, DiMaio F, Anishchenko I et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:871–876. https://doi.org/10.1126/science.abj8754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Baek M, Baker D (2022) Deep learning and protein structure modeling. Nat Methods 19:13–14. https://doi.org/10.1038/s41592-021-01360-8

    Article  CAS  PubMed  Google Scholar 

  43. Li Z, Jaroszewski L, Iyer M et al (2020) FATCAT 2.0: towards a better understanding of the structural diversity of proteins. Nucleic Acids Res 48:W60–W64. https://doi.org/10.1093/nar/gkaa443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sehnal D, Bittrich S, Deshpande M et al (2021) Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures. Nucleic Acids Res 49:W431–W437. https://doi.org/10.1093/nar/gkab314

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sun Q, Kuty GF, Arockiasamy A et al (2009) Regulation of a muralytic enzyme by dynamic membrane topology. Nat Struct Mol Biol 16:1192–1194. https://doi.org/10.1038/nsmb.1681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Huang Y, Yang H, Yu J, Wei H (2015) Molecular dissection of phage lysin PlySs2: integrity of the catalytic and cell wall binding domains is essential for its broad lytic activity. Virol Sin 30:45–51. https://doi.org/10.1007/s12250-014-3535-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hu S, Kong J, Kong W et al (2010) Characterization of a novel LysM domain from Lactobacillus fermentum bacteriophage endolysin and its use as an anchor to display heterologous proteins on the surfaces of lactic acid bacteria. Appl Environ Microbiol 76:2410–2418. https://doi.org/10.1128/AEM.01752-09

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

MTPG is supported by the Brazilian funding agency Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP) through an M.Sc. scholarship (grant 2020/01535-9). MPT is also supported by FAPESP through a scientific initiation scholarship (grant 2020/09815-0). Research in our laboratory is supported by FAPESP (grant 2021/00465-0). MB is a research fellow of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq – process number: 309380/2019-7).

Author information

Authors and Affiliations

Authors

Contributions

MTPG, MPT, and PMPV conceived and designed the study, performed the analysis, and interpreted the data. MTPG wrote the manuscript. MB contributed to finalizing the manuscript and coordinated the study. All authors read and approved the final text.

Corresponding author

Correspondence to Marco Túlio Pardini Gontijo.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (FASTA 30 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gontijo, M.T., Teles, M.P., Vidigal, P.M. et al. Expanding the Database of Signal-Anchor-Release Domain Endolysins Through Metagenomics. Probiotics & Antimicro. Prot. 14, 603–612 (2022). https://doi.org/10.1007/s12602-022-09948-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12602-022-09948-y

Keywords

Navigation