Analysis of Tumor Necrosis Factor Function Using the Resonant Recognition Model
Irena Cosic1,2, Drasko Cosic3, Katarina Lazar3
1RMIT University, La Trobe Street, Melbourne, VIC, 3000, Australia. irena.cosic@rmit.edu.au.
Abstract:
The tumor necrosis factor (TNF) is a complex protein that plays a very important role in a number of biological functions including apoptotic cell death, tumor regression, cachexia, inflammation inhibition of tumorigenesis and viral replication. Its most interesting function is that it is an inhibitor of tumorigenesis and inductor of apoptosis. Thus, the TNF could be a good candidate for cancer therapy. However, the TNF has also inflammatory and toxic effects. Therefore, it would be very important to understand complex functions of the TNF and consequently be able to predict mutations or even design the new TNF-related proteins that will have only a tumor inhibition function, but not other side effects. This can be achieved by applying the resonant recognition model (RRM), a unique computational model of analysing macromolecular sequences of proteins, DNA and RNA. The RRM is based on finding that certain periodicities in distribution of free electron energies along protein, DNA and RNA are strongly correlated to the biological function of these macromolecules. Thus, based on these findings, the RRM has capabilities of protein function identification, prediction of bioactive amino acids and protein design with desired biological function. Using the RRM, we separate different functions of TNF as different periodicities (frequencies) within the distribution of free energy electrons along TNF protein. Interestingly, these characteristic TNF frequencies are related to previously identified characteristics of proto-oncogene and oncogene proteins describing TNF involvement in oncogenesis. Consequently, we identify the key amino acids related to the crucial TNF function, i.e. receptor recognition. We have also designed the peptide which will have the ability to recognise the receptor without side effects.
Insights
Tumor necrosis factor (TNF) has dual roles in cancer. The resonant recognition model (RRM) computational approach helps differentiate TNF
Area of Science:
- * Molecular Biology
- * Computational Biology
- * Bioinformatics
Background:
- * Tumor necrosis factor (TNF) is a protein with critical roles in apoptosis, inflammation, and tumor suppression.
- * While TNF shows promise for cancer therapy, its inflammatory and toxic side effects necessitate a deeper functional understanding.
- * Differentiating TNF's beneficial and detrimental functions is key to developing targeted cancer treatments.
Purpose of the Study:
- * To elucidate the complex functions of tumor necrosis factor (TNF) using a computational approach.
- * To identify specific TNF functions, such as tumor inhibition and apoptosis induction, and their associated molecular characteristics.
- * To explore the potential for designing novel TNF-related proteins with enhanced therapeutic efficacy and reduced side effects.
Main Methods:
- * Application of the resonant recognition model (RRM), a computational tool for analyzing macromolecular sequences.
- * Analyzing periodicities in the distribution of free electron energies along the TNF protein sequence.
- * Correlating identified periodicities (frequencies) with specific biological functions of TNF.
Main Results:
- * Different functions of TNF were successfully separated and identified as distinct periodicities within its free electron energy distribution.
- * Characteristic TNF frequencies were found to correlate with proto-oncogene and oncogene protein characteristics, linking TNF to oncogenesis.
- * Key amino acids responsible for TNF's receptor recognition function were identified.
Conclusions:
- * The resonant recognition model (RRM) can effectively differentiate complex protein functions, including those of TNF.
- * Understanding TNF's functional periodicities provides insights into its role in oncogenesis and its potential as a cancer therapeutic.
- * The study successfully designed a peptide capable of receptor recognition, demonstrating the potential for developing targeted TNF-based therapies with minimized side effects.
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