JIOMICS

JIOMICS | VOL 5 | ISSUE 2 | DECEMBER 2015 | 1-62
JOURNAL OF INTEGRATED OMICS
Journal of Integrated Omics
A METHODOLOGICAL JOURNAL
HTTP://WWW.JIOMICS.COM
Special Issue: Proceeding Abstracts of the 4 th International Congress on Analytical Proteomics (ICAP 2015)
The Resonant Recognition model to design short bioactive therapeutic
peptides: does it really work?
T. lodeir* 1 , E. Pirogova 2
1
Biosciences, School of Applied Sciences, RMIT University, Bundoora, Victoria, 3083, Australia. 2 Biomedical Engineering, School of Electrical
and Computer Engineering, RMIT University, Melbourne, Victoria, 3000, Australia. *Corresponding author: taghrid.istivan@rmit.edu.au
Available Online: 31 December 2015
Abstract
In recent years, more focus has been placed on the role of small molecular weight peptides in clinical medicine, mostly for their ability to penetrate
cellular membranes, and to interfere with enzymatic functions or protein-protein interactions. The ability to predict the 3D structures and
functions of biological molecules would certainly be useful in designing therapeutic drugs. Hence, the Resonant Recognition Model (RRM),
applied in this study, is a physico-mathematical model that incorporates signal processing methodology for structure-function analysis of proteins.
The aim of our research is to design and investigate the biological effects of RRM bioactive peptides and their potential to be applied as
therapeutic agents.
Experimental description: The RRM approach was used to design 18-22 mer bioactive peptide analogues of natural proteins known to possess
cytotoxic effects on cancer cells including Myxoma virus (MV); mammalian interleukins (IL) and tumor necrosis factor (TNFα). The biological
effects of RRM-MV; RRM-IL; and RRM-TNF were investigated on mammalian cancer and normal cell lines. The cytotoxic effects were
evaluated by qualitative and quantitative cell survival methods. In addition non-bioactive peptides lacking the specific RRM frequencies or
peptides with scrambled sequences were also tested as controls. Human apoptosis protein arrays were used to detect the expression levels of
pro-apoptotic and anti-apoptotic proteins in treated versus non-treated cancer cells. Glycomic arrays and fluorescent microscopy were used to
find the potential cellular targets of these peptides in cancer cells. Furthermore, we also applied the RRM to design short peptides with antimicrobial
activity as analogues for known antimicrobial natural peptides like azurocidin (CAP37) and lactoferrins and evaluated their effect on
Escherichia coli, and Staphylococci including a methicillin resistant S. aureus (MRSA) strain.
Results: The bioactive peptides RRM-MV, RRM-IL, and RRM-TNF produced significant apoptotic/necrotic effects on B16 mouse melanoma
cells, human melanoma MM96L, and COLO16squamous cell carcinoma, PC3 prostate cancer and MCF7 breast cancer. However, no cytotoxic
effects were detected on human red blood cells, normal skin fibroblasts; mouse macrophages, mouse fibroblasts, and Chinese hamster ovary
cells (CHO). Yet the non-bioactive, control peptides did not cause any cytotoxic changes in any type of cancer or normal cells. The bioactive
peptides were found to be located inside cytoplasmic components in treated cancer cells prior to necrosis stage. Possible cellular targets were
predicted to be specific glycoproteins, however the bioactive peptides have a different binding specificity to glycans as Sialy Lewis X, gangliosides
and mannoses. Furthermore, the azurocidin peptide analogue had a bacteriostatic antimicrobial effect on all tested bacteria including
MRSA.
Conclusions: The RRM is a powerful computational tool to design bioactive peptides with specific biological functions. Our data indicates
that each of the RRM designed peptides possessed the desired biological function which was conserved in its unique sequence.
Keywords: Resonant recognition model, RRM-designed therapeutic peptides, anticancer; antimicrobial.
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