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Prediction of improved antimicrobial mastoparan derivatives by 3D-QSAR-CoMSIA/CoMFA and computational mutagenesis

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Abstract

Antimicrobial peptides are an important class of therapeutic agents used against a wide range of pathogens such as gram-negative and -positive bacteria, fungi, and viruses. The minimal inhibitory concentration at the level of the pathogen membrane is a major determinant of the pharmacokinetic behavior and, consequently, it can affect their antimicrobial activity. Here we generated quantitative structure-activity relationship models (3D-QSAR—comparative molecular field analysis/comparative molecular similarity indices analysis) using a database of 33 mastoparan analogs, antimicrobial peptides with known experimental activity, and further used these models to predict the minimal inhibitory concentration for 18 new mastoparan analogs, obtained by computational mutagenesis. We discuss two options for structural alignment of mastoparan analogs: superposition of Cα trace atoms or superposition of all backbone atoms. Significant values of the cross-validated correlation q 2 (higher than 0.60) and the fitted correlation r 2 (higher than 0.90) of our models indicate that they are reliable enough for activity prediction in the case of new derivatives. This allows us to identify compounds with possibly enhanced antimicrobial activity against Bacillus subtilis, which are suggested for further experimental studies.

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References

  1. Hilpert K, Elliott MR, Volkmer-Engert R, Henklein P, Donini O, Zhou Q, Winkler DF, Hancock RE (2006) Chem Biol 13:1101

    Article  CAS  Google Scholar 

  2. Rodriguez CH, De Ambrosio A, Bajuk M, Spinozzi M, Nastro M, Bombicino K, Radice M, Gutkind G, Vay C, Famiglietti A (2010) J Infect Dev Countries 4:164

    CAS  Google Scholar 

  3. Bals R, Hubert D, Tummler B (2011) J Cystic Fibrosis 10(Suppl 2):S146

    Article  CAS  Google Scholar 

  4. Opatowski L, Guillemot D, Boelle PY, Temime L (2011) Curr Opin Infect Dis 24:279

    Article  Google Scholar 

  5. Zhu WL, Song YM, Park Y, Park KH, Yang ST, Kim JI, Park IS, Hahm KS, Shin SY (2007) Biochim Biophys Acta 1768:1506

    Article  CAS  Google Scholar 

  6. Zasloff M (2002) Nature 415:389

    Article  CAS  Google Scholar 

  7. Zanetti M, Gennaro R, Skerlavaj B, Tomasinsig L, Circo R (2002) Curr Pharm Design 8:779

    Article  CAS  Google Scholar 

  8. Kolar SS, McDermott AM (2011) Cell Mol Life Sci 68:2201

    Article  CAS  Google Scholar 

  9. Bernard JJ, Gallo RL (2011) Cell Mol Life Sci 68:2189

    Article  CAS  Google Scholar 

  10. Maher S, McClean S (2006) Biochem Pharmacol 71:1289

    Article  CAS  Google Scholar 

  11. Rosenfeld Y, Lev N, Shai Y (2010) Biochemistry 49:853

    Article  CAS  Google Scholar 

  12. Harris F, Dennison SR, Phoenix DA (2009) Curr Protein Pept Sci 10:585

    Article  CAS  Google Scholar 

  13. Hancock RE, Rozek A (2002) FEMS Microbiol Lett 206:143

    Article  CAS  Google Scholar 

  14. Jin Y, Hammer J, Pate M, Zhang Y, Zhu F, Zmuda E, Blazyk J (2005) Antimicrob Agents Chemother 49:4957

    Article  CAS  Google Scholar 

  15. Mahalka AK, Kinnunen PK (2009) Biochim Biophys Acta 1788:1600

    Article  CAS  Google Scholar 

  16. Leptihn S, Har JY, Wohland T, Ding JL (2010) Biochemistry 49:9161

    Article  CAS  Google Scholar 

  17. Yin F, Kindt JT (2010) J Phys Chem B 114:8076

    Article  CAS  Google Scholar 

  18. Cruciani RA, Barker JL, Zasloff M, Chen HC, Colamonici O (1991) Proc Natl Acad Sci USA 88:3792

    Article  CAS  Google Scholar 

  19. Mally M, Majhenc J, Svetina S, Zeks B (2007) Biochim Biophys Acta 1768:1179

    Article  CAS  Google Scholar 

  20. Dempsey CE, Hawrani A, Howe RA, Walsh TR (2010) Protein Pept Lett 17:1334

    CAS  Google Scholar 

  21. Nan YH, Bang JK, Shin SY (2009) Peptides 30:832

    Article  CAS  Google Scholar 

  22. Cerovsky V, Slaninova J, Fucik V, Hulacova H, Borovickova L, Jezek R, Bednarova L (2008) Peptides 29:992

    Article  CAS  Google Scholar 

  23. Cerovsky V, Pohl J, Yang Z, Alam N, Attygalle AB (2007) J Pept Sci 13:445

    Article  CAS  Google Scholar 

  24. dos Santos Cabrera MP, Costa ST, de Souza BM, Palma MS, Ruggiero JR, Ruggiero Neto J (2008) Eur Biophys J 37:879

  25. Cabrera MP, Alvares DS, Leite NB, de Souza BM, Palma MS, Riske KA, Neto JR (2011) Amino Acids 40:77

    Article  Google Scholar 

  26. Leite NB, da Costa LC, dos Santos Alvares D, dos Santos Cabrera MP, de Souza BM, Palma MS, Neto JR (2011) Amino Acids 40:91

  27. Mikut R (2010) Methods Mol Biol 618:287

    Article  CAS  Google Scholar 

  28. Tong J, Liu S, Zhou P, Wu B, Li Z (2008) J Theor Biol 253:90

    Article  CAS  Google Scholar 

  29. Jenssen H, Fjell CD, Cherkasov A, Hancock RE (2008) J Pept Sci 14:110

    Article  CAS  Google Scholar 

  30. Collantes ER, Dunn WJ (1995) J Med Chem 38:2705

    Article  CAS  Google Scholar 

  31. Avram S, Duda-Seiman D, Borcan F, Radu B, Duda-Seiman C, Mihailescu D (2011) Int J Pept Res Ther 17:7

    Article  CAS  Google Scholar 

  32. Persson B (2000) EXS 88:215

    CAS  Google Scholar 

  33. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) J Am Chem Soc 117:5179

    Article  CAS  Google Scholar 

  34. Akamatsu M (2002) Curr Top Med Chem 2:1381

    Article  CAS  Google Scholar 

  35. Checler F, Alves da Costa C, Ayral E, Andrau D, Dumanchin C, Farzan M, Hernandez JF, Martinez J, Lefranc-Jullien S, Marambaud P, Pasini A, Petit A, Phiel C, Robert P, St. George-Hyslop P, Wilk S (2005) Curr Alzheimer Res 2:327

  36. Sybyl 7 (2010) http://www.tripos.com; accessed 21 Oct 2011

  37. Hayashi Y, Sakaguchi K, Kobayashi M, Kikuchi Y, Ichiishi E (2003) Bioinformatics 19:1514

    Article  CAS  Google Scholar 

  38. Oprea TI, Waller CL, Marshall GR (1994) J Med Chem 37:2206

    Article  CAS  Google Scholar 

  39. Cramer RD, Patterson DE, Bunce JD (1989) Prog Clin Biol Res 291:161

    CAS  Google Scholar 

  40. Klebe G, Abraham U, Mietzner T (1994) J Med Chem 37:4130

    Article  CAS  Google Scholar 

  41. Khedkar SA, Malde AK, Coutinho EC (2007) J Mol Model 13:1099

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We acknowledge the financial support of CNMP PNII 61-016/2007, CNMP PNII 62-061/2008, and PNII PD-586/2010. A.-L. Milac was supported by the Romanian Academy project 3 of the Institute of Biochemistry of the Romanian Academy. A.-L. Milac acknowledges the postdoctoral program POSDRU/89/1.5/S/60746 from the European Social Fund.

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Authors and Affiliations

  1. Institute of Biochemistry of the Romanian Academy, 296th Spl. Independentei, 060031, Bucharest, Romania

    Adina-Luminita Milac

  2. Faculty of Biology, Anatomy, Animal Physiology and Biophysics Department, University of Bucharest, 91-95th Spl. Independentei, 076201, Bucharest, Romania

    Speranta Avram & Dan Mihailescu

  3. Pharmaceutical Chemistry Department, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy, 2nd E. Murgu Sq., 300041, Timisoara, Romania

    Florin Borcan

Authors
  1. Speranta Avram
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  2. Dan Mihailescu
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  3. Florin Borcan
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  4. Adina-Luminita Milac
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Correspondence to Adina-Luminita Milac.

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Avram, S., Mihailescu, D., Borcan, F. et al. Prediction of improved antimicrobial mastoparan derivatives by 3D-QSAR-CoMSIA/CoMFA and computational mutagenesis. Monatsh Chem 143, 535–543 (2012). https://doi.org/10.1007/s00706-011-0713-1

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  • DOI: https://doi.org/10.1007/s00706-011-0713-1

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