In Silico Derived Peptides for Inhibiting the Toxin–Antitoxin Systems of Mycobacterium tuberculosis: Basis for Developing Peptide-Based Therapeutics

  • Shobana SundarEmail author
  • Madhu Pearl Rajan
  • Shanmughavel Piramanayagam


Toxin–antitoxin (TA) systems of Mycobacterium tuberculosis (Mtb) is a prerequisite for the bacterium to survive in extreme conditions. Antimicrobial peptides inhibiting the formation of these complexes provide a novel strategy for TB drug discovery process. Absence of TA genes in human, makes these systems as an attractive target for drug development. In this study using Peptiderive server, we have derived a number of potential inhibitory peptides for nine TA complexes—VapBC3, VapBC5, VapBC11, VapBC15, VapBC26, VapBC30, RelBE2, RelJK, MazEF4 of Mtb. We have studied about the common interacting toxin residues with the antitoxin and with the derived peptide. Further, using Cluspro server, we compared the binding efficacy of the in silico derived peptides with the published potential peptides for the toxins VapC26, VapC30 and MazF. Thus, these in silico derived peptides would serve as basis for developing peptide based therapeutics for TA complexes of Mtb.


Toxin–antitoxin complexes In silico derived peptides Peptide based therapeutics Mycobacterium tuberculosis 



The authors thank the Department of Biotechnology (DBT), New Delhi for providing the Bioinformatics Infrastructure facility (DBT-BIF) to carry out this study successfully. We also acknowledge the DBT-Centre for Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India for providing all the computational facilities to carry out this work.


Shobana Sundar acknowledges the financial support through the award of Research Associateship from DBT, Award Letter No: C3/20541/2018.

Compliance with Ethical Standards

Conflict of interest

All the authors declare that they have none conflict of interest.

Research Involving Human And Animal Participants

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Ahn DH, Lee KY, Lee SJ, Park SJ, Yoon HJ, Kim SJ, Lee BJ (2017) Structural analyses of the MazEF4 toxin-antitoxin pair in Mycobacterium tuberculosis provide evidence for a unique extracellular death factor. J Biol Chem 292:18832–18847CrossRefPubMedPubMedCentralGoogle Scholar
  2. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bruzzoni-Giovanelli H, Alezra V, Wolff N, Dong CZ, Tuffery P, Rebollo A (2017) Interfering peptides targeting protein–protein interactions: the next generation of drugs? Drug Discov Today. CrossRefPubMedGoogle Scholar
  4. Kang SM, Kim DH, Lee KY, Park SJ, Yoon HJ, Lee SJ, Hookang I, Lee BJ (2017) Functional details of the Mycobacterium tuberculosis VapBC26 toxin-antitoxin system based on a structural study: insights into unique binding and antibiotic peptides. Nucleic acids Res 45:8564–8580CrossRefPubMedPubMedCentralGoogle Scholar
  5. Korch SB, Hill TM (2006) Ectopic overexpression of wild-type and mutant hipA genes in Escherichia coli: effects on macromolecular synthesis and persister formation. J Bacteriol 188:3826–3836CrossRefPubMedPubMedCentralGoogle Scholar
  6. Korch SB, Contreras H, Clark-Curtiss JE (2009) Three Mycobacterium tuberculosis Rel toxin-antitoxin modules inhibit mycobacterial growth and are expressed in infected human macrophages. J Bacteriol 191:1618–1630CrossRefPubMedGoogle Scholar
  7. Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12:255–278CrossRefPubMedPubMedCentralGoogle Scholar
  8. Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P (2016) PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res 44:W449–W454CrossRefPubMedPubMedCentralGoogle Scholar
  9. Laskowski RA (2001) PDBsum: summaries and analyses of PDB structures. Nucleic acids Res 29:221–222CrossRefPubMedPubMedCentralGoogle Scholar
  10. Lee IG, Lee SJ, Chae S, Lee KY, Kim JH, Lee BJ (2015) Structural and functional studies of the Mycobacterium tuberculosis VapBC30 toxin-antitoxin system: implications for the design of novel antimicrobial peptides. Nucleic Acids Res 43:7624–7637CrossRefPubMedPubMedCentralGoogle Scholar
  11. Ogura T, Hiraga S (1983) Mini-F plasmid genes that couple host cell division to plasmid proliferation. Proc Natl Acad Sci USA 80:4784–4788CrossRefPubMedGoogle Scholar
  12. Page R, Peti W (2016) Toxin-antitoxin systems in bacterial growth arrest and persistence. Nat Chem Biol 12:208–214CrossRefPubMedGoogle Scholar
  13. PyMOL. The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLCGoogle Scholar
  14. Sala A, Bordes P, Genevaux P (2014) Multiple toxin-antitoxin systems in Mycobacterium tuberculosis. Toxins (Basel) 6:1002–1020CrossRefPubMedPubMedCentralGoogle Scholar
  15. Sedan Y, Marcu O, Lyskov S, Schueler-Furman O (2016) Peptiderive server: derive peptide inhibitors from protein–protein interactions. Nucleic Acids Res. CrossRefPubMedPubMedCentralGoogle Scholar
  16. Shao Y, Harrison EM, Bi D, Tai C, He X, Ou HY, Rajakumar K, Deng Z (2011) TADB: a web-based resource for Type 2 toxin-antitoxin loci in bacteria and archaea. Nucleic Acids Res 39:D606–D611CrossRefPubMedGoogle Scholar
  17. Wang X, Wood TK (2011) Toxin-antitoxin systems influence biofilm and persister cell formation and the general stress response. Appl Environ Microbiol 77:5577–5583CrossRefPubMedPubMedCentralGoogle Scholar
  18. WHO Global tuberculosis report 2017Google Scholar
  19. Williams JJ, Hergenrother PJ (2012) Artificial activation of toxin–antitoxin systems as an antibacterial strategy. Trends Microbiol 20:291–298CrossRefPubMedPubMedCentralGoogle Scholar
  20. Yamaguchi Y, Inouye M (2009) mRNA interferases, sequence-specific endoribonucleases from the toxin-antitoxin systems. Prog Mol Biol Transl Sci 85:467–500CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of BioinformaticsBharathiar UniversityCoimbatoreIndia
  2. 2.Computational Biology Lab, Department of BioinformaticsBharathiar UniversityCoimbatoreIndia

Personalised recommendations