Skip to main content

Mathematical Algorithms for Finding the Optimal Composition of the Amino Acid Composition of Peptides Used as a Therapy

  • Chapter
  • First Online:
  • 708 Accesses

Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

In this chapter, two algorithms have been developed, one of which (Algorithm 3) was developed specifically for the selection of amino acid residues in peptides to improve their affinity in the interaction of peptides with full-length proteins, and Algorithm 4 was developed to search for “scattered” active region of the protein when bound to the peptide.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. J. Ferlay, H.R. Shin, F. Bray, D. Forman, C. Mathers, D.M. Parkin, Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127(12), 2893–2917 (2010)

    Article  Google Scholar 

  2. J. Ferlay, E. Steliarova-Foucher, J. Lortet-Tieulent, S. Rosso, J.W. Coebergh, H. Comber, D. Forman, F. Bray, Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur. J. Cancer 49(6), 1374–1403 (2013)

    Article  Google Scholar 

  3. M. Arnold, M.S. Sierra, M. Laversanne, I. Soerjomataram, A. Jemal, F. Bray, Global patterns and trends in colorectal cancer incidence and mortality. Gut 66(4), 683–691 (2017)

    Article  Google Scholar 

  4. J. Thundimadathil, Cancer treatment using peptides: current therapies and future prospects. J. Amino Acids (2012)

    Google Scholar 

  5. R. Domalaon, B. Findlay, M. Ogunsina, G. Arthur, F. Schweizer, Ultrashort cationic lipopeptides and lipopeptoids: evaluation and mechanistic insights against epithelial cancer cells. Peptides 84, 58–67 (2016)

    Article  Google Scholar 

  6. S.R. Dennison, F. Harris, D.A. Phoenix, The interactions of aurein 1.2 with cancer cell membranes. Biophys. Chem. 127(1–2), 78–83 (2007)

    Article  Google Scholar 

  7. M.R. Felicio, O.N. Silva, S. Goncalves, N.C. Santos, O.L. Franco, Peptides with dual antimicrobial and anticancer activities. Front. Chem. 5 (2017)

    Google Scholar 

  8. A. Jemal, F. Bray, M.M. Center, J. Ferlay, E. Ward, D. Forman, Global cancer statistics. CA Cancer J. Clin. 61(2), 69–90 (2011)

    Article  Google Scholar 

  9. Baguley B. C. Multiple drug resistance mechanisms in Cancer// Mol. Biotechnol. 2010. V.46. N.3. Pp.308–316

    Google Scholar 

  10. L.A. Mandell, P. Ball, G. Tillotson, Antimicrobial safety and tolerability: differences and dilemmas. Clin. Infect. Dis. 32(1), S72–9 (2001)

    Article  Google Scholar 

  11. C.R. Lincke, A.M. van der Bliek, G.J. Schuurhuis, T. van der Velde-Koerts, J.J. Smit, P. Borst, Multidrug resistance phenotype of human BRO melanoma cells transfected with a wild-type human mdr1 complementary DNA. Cancer Res. 50(6), 1779–1785 (1990)

    Google Scholar 

  12. C.A. Arias, B.E. Murray, Antibiotic-resistant bugs in the 21st century - a clinical super-challenge. N. Engl. J. Med. 360(5), 439–443 (2009)

    Article  Google Scholar 

  13. D. Kakde, D. Jain, V. Shrivastava, R. Kakde, A.T. Patil, Cancer therapeutics-opportunities, challenges and advances in drug delivery. J. Appl. Pharm. Sci. 1, 1–10 (2011)

    Google Scholar 

  14. S. Mocellin, P. Pilati, D. Nitti, Peptide-based anticancer vaccines: recent advances and future perspectives. Curr. Med. Chem. 16(36), 4779–4796 (2009)

    Article  Google Scholar 

  15. J.N. Francis, M. Larch, Peptide-based vaccination: where do we stand? Curr. Opin. Allergy Clin. Immunol. 5(6), 537–543 (2005)

    Article  Google Scholar 

  16. Y.-F. Xiao, M.-M. Jie, B.-S. Li, C.-J. Hu, R. Xie, B. Tang, S.-M. Yang, Peptide-based treatment: a promising cancer therapy. J. Immunol. Res. 2015. Article ID 761820 (2015) 13 pp

    Google Scholar 

  17. Y. Yoshitake, D. Fukuma, A. Yuno, M. Hirayama, H. Nakayama, T. Tanaka, M. Nagata, Y. Takamune, K. Kawahara, Y. Nakagawa, R. Yoshida, A. Hirosue, H. Ogi, A. Hiraki, H. Jono, A. Hamada, K. Yoshida, Y. Nishimura, Y. Nakamura, M. Shinohara, Phase II clinical trial of multiple peptide vaccination for advanced head and neck cancer patients revealed induction of immune responses and improved OS. Clin. Cancer. Res. 21(2), 312–321 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kirill Kulikov .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Koshlan, T., Kulikov, K. (2018). Mathematical Algorithms for Finding the Optimal Composition of the Amino Acid Composition of Peptides Used as a Therapy. In: Mathematical Modeling of Protein Complexes. Biological and Medical Physics, Biomedical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-98304-2_8

Download citation

Publish with us

Policies and ethics