With Resonant Recognition Model frequency and phase identified for particular function/interaction we can:

 

• analyse protein biological function;

• predict key amino acids (“hot spots mutations”) for the particular function;   

• design de novo peptides with the desired biological function or interaction ability;

 

This approach has been already proved in number of examples including:

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We have designed de-novo peptide that can inhibit beta-lactamase in resistant bacteria.

 

In the case of COVID-19, which is caused by single stranded RNA virus SARS-CoV-2 and attacks cells trough ACE2 receptor, we have designed 18-mer peptide to prevent this interaction. This peptide was tested in vitro by Inhibitor Screening Assay Kits, as well as on Vero E6 cell line, and has shown ability to significantly prevent interaction between SARS-CoV-2 virus and ACE2 receptor.

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In the case of Fibroblast Growth Factors (FGF), two RRM characteristic frequencies were identified: one related to receptor recognition and another related to “growth activity”. The aim of that particular project was to design peptide which can competitively bind to the FGF receptor but without inducing growth. Using only receptor recognition frequency the 16-mer peptide was designed, experimentally tested and indeed had receptor recognition activity without inducing growth.

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In the case of HIV virus, the one common RRM frequency was identified for all HIV envelope protein despite their high variability. This frequency was used to design peptide that can immunologically mimic all HIV isolates and thus could be a good candidate for vaccine.

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In the case of interleukin-12, which can significantly affect tumour progression, the RRM model was used to design de-novo interleukin-12 like peptide. This peptide has been synthetised and tested experimentally on cancer cell line affecting cancer cell growth and survival causing apoptosis.   

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Similar idea was used to mimic myxoma virus oncolytic function. Myxoma virus (MV) is rabbit-specific poxvirus pathogen that also exhibits unique tropism for human tumor cells and is dramatically oncolytic for human cancer xenografts. The RRM characteristic frequency for MV proteins was identified and used to design peptides that were experimentally shown to mimic myxoma virus oncolytic function.

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References:

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1. Mishra A, Cosic I, Loncarevic I, Cosic D,Fletcher HM: Inhibition of β-lactamase function by de novo designed peptide. PLoS ONE, 2023, 18(9), 1-15, doi: 10.1371/journal.pone.0290845.

2. Cosic I, Kuhar U, Krapez U, Slavec B, Cosic D, Loncarevic I: De Novo Designed Peptide to Prevent SARS-CoV-2 Interaction with ACE2 Receptor on Host Cells. International Journal of Sciences, 2022, 11(2), 1-8, doi: 10.18483/ijSci.2558.

3. Almansour N, Pirogova E, Coloe P, Cosic I,Istivan T: Investigation of cytotoxicity of negative control peptides versus bioactive peptides on skin cancer and normal cells: a comparative study, Future Medicinal Chemistry, 2012; 4(12), pp: 1553-1565.

4. Almansour N, Pirogova E, Coloe P, Cosic I, Istivan T: A bioactive peptide analogue for myxoma virus protein with a targeted cytotoxicity for human skin cancer in vitro, Journal of Biomedical Science, 2012; 19(65), pp: 1-13.

5. Pirogova E, Vojisavljevic V, Cosic I:Rational computational approaches to studying inhibition and activation of metalloproteinase enzymes using signal processing techniques, MD-Medical Data, 2011; 3(1),  pp: 5-9, http://md-medicaldata.com/godina_2011_broj_1.html

6. Pirogova E, Vojisavljevic V, Cosic I: Investigating natural mutagenesis of the H1N1 virus and relationships between its mutants and virulence of the virus using a computational approach, MD-Medical Data, 2011; 3(2), pp: 111-117, http://md-medicaldata.com/godina_2011_broj_2.html

7. Istivan T, Pirogova E, Gan E, Almansour N, Coloe P, Cosic I: Biological effects of a De Novo designed myxoma virus peptide analogue: Evaluation of cytotoxicity on tumor cells, Public Library of Science (PLoS) ONE, 2011; 6(9), pp: 1-10.

8. Pirogova E, Istivan T, Gan E, Cosic I: Advances in methods for therapeutic peptide discovery, design and development, in Current Pharmaceutical Biotechnology, Bentham Science Publishers Ltd., Netherlands, 2011; ISSN: 1389-2010, 12(8), pp: 1117-1127.

9. Pirogova E, Vojisavljevic V, Istivan T, Coloe P, Cosic I:Reveiw study: influence of electromagnetic radiation on enzyme activity and effects of synthetic peptides on cell transformation, MD-Medical Data, 2010; 2(4), pp: 317-324, http://md-medicaldata.com/godina_2010_broj_4.html

10. Pirogova E, Istivan T, Gan E, Coloe P, Cosic I: Computationally Designed Interleukin-Like Peptide as Candidate for Cancer Treatment, IFMBE procedings, 2009; 25(4), doi: 10.1007/978-3-642-03882-2_524.

11. Cosic I, Pirogova E: Bioactive Peptide Design using the Resonant Recognition Model, Nonlinear Biomedical Physics, 2007; 1(7), doi: 10.1186/1753-4631-1-7.

12. Krsmanovic V, Biquard JM, Sikorska-Walker M, Cosic I, Desgranges C, Trabaud MA, Whitfield JF, Durkin JP, Achour A, Hearn MT: Investigation Into the Cross-reactivity of Rabbit Antibodies Raised against Nonhomologous Pairs of Synthetic Peptides Derived from HIV-1 gp120 proteins, J.Peptide Res., 1998; 52(5), pp: 410-412.

13. Cosic I: Virtual Spectroscopy for Fun and Profit, Biotechnology, 1995; 13, pp: 236-238.

14. Cosic I, Drummond AE, Underwood JR, Hearn MTW: A New Approach to Growth Factor Analogue Design: Modelling of FGF Analogues, Molecular and Cellular Biochemistry, 1994; 130, pp: 1-9.

15. Cosic I: Macromolecular Bioactivity: Is it Resonant Interaction Between Macromolecules? - Theory and Applications, IEEE Trans. on Biomedical Engineering, 1994: 41, pp: 1101-1114.