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.

Background: It has been shown that there are electromagnetic resonances in biological molecules (proteins, DNA and RNA) in the wide range of frequencies including THz, GHz, MHz and KHz. These resonances could be important for biological function of macromolecules, as well as could be used in development of devices like molecular computers. As experimental measurements of macromolecular resonances are timely and costly there is a need for computational methods that can reliably predict these resonances. We have previously used the Resonant Recognition Model (RRM) to predict electromagnetic resonances in tubulin and microtubules. Consequently, these predictions were confirmed experimentally. Methods: The RRM is developed by authors and is based on findings that protein, DNA and RNA electromagnetic resonances are related to the free electron energy distribution along the macromolecule. Results: Here, we applied the Resonant Recognition Model (RRM) to predict possible electromagnetic resonances in telomerase as an example of protein, telomere as an example of DNA and TERT mRNA as an example of RNA macromolecules. Conclusion: We propose that RRM is a powerful model that can computationally predict protein, DNA and RNA electromagnetic resonances.

Tubulin proteins were analyzed using the Resonant Recognition Model to predict possible electromagnetic resonances in tubulin and microtubules. We propose that these electromagnetic resonances are caused by charge transfer through the protein molecule. The frequencies of these electromagnetic resonances depend on charge velocity. Using different velocities of charge transfer, we predicted resonant frequencies in different frequency ranges from KHz to THz. These resonant frequencies could be relevant for taxol binding as well as a possible role of microtubules as a macromolecular computer.

Abstract:The meaning and influence of light to biomolecular interactions, and consequently to health, has been analyzed using the Resonant Recognition Model (RRM). The RRM proposes that biological processes/interactions are based on electromagnetic resonances between interacting biomolecules at specific electromagnetic frequencies within the infra-red, visible and ultra-violet frequency ranges, where each interaction can be identified by the certain frequency critical for resonant activation of specific biological activities of proteins and DNA. We found that: (1) the various biological interactions could be grouped according to their resonant frequency into super families of these functions, enabling simpler analyses of these interactions and consequently analyses of influence of electromagnetic frequencies to health; (2) the RRM spectrum of all analyzed biological functions/interactions is the same as the spectrum of the sun light on the Earth, which is in accordance with fact that life is sustained by the sun light; (3) the water is transparent to RRM frequencies, enabling proteins and DNA to interact without loss of energy; (4) the spectrum of some artificial sources of light, as opposed to the sun light, do not cover the whole RRM spectrum, causing concerns for disturbance to some biological functions and consequently we speculate that it can influence health.

Abstract. It is documented that the large number of mutations within BRCA-1 and BRCA-2 genes are related to development of breast cancer, ovarian cancer, as well as prostate cancer and pancreatic cancer. However, it is not known which mutations are the most critical for formation of these cancers. We have analysed human BRCA-1, BRCA-2 and related RAD51 protein functions and functional mutations using our previously developed Resonant Recognition Model (RRM). The RRM is capable to analyse protein biological functions/interactions, predict bioactive mutations and design de novo bioactive peptides with desired biological function. The most critical mutations for formation of cancer in human BRCA-1, BRCA-2 and RAD51 proteins have been predicted and compared with experimental results. The predicted mutations within 3D structures have been presented and discussed. Such findings can lead to development of much simpler and more relevant tests for genetic predisposition to breast, ovarian, prostate and pancreatic cancers.