Analysis of Tumor Necrosis Factor Function Using the Resonant Recognition Model
Authors: Drasko Cosic
Katarina Lazar
Irena Cosic
Journal: Cell Biochemistry and Biophysics (2016)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Amino acid; DNA; Fast Fourier transform; Oncogenes; Protein
Affiliations: RMIT University, La Trobe Street, Melbourne, VIC, 3000, Australia. ., AMALNA Consulting, 46 Second St, Black Rock, VIC, 3193, Australia.

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. 

Retracted: No

PubPeer comments:
 On behalf of commenter *Orchestes quercus*, I report here a few considerations on the RRM model used in this article. The Resonant Recognition Model (RRM) is lacking physical basis. The idea behind the model goes to go back to a 1980 book publication by V. Veljkovic (1). I unfortunately cannot read this 40+ year old seminal work, but from a [short review] it is clear that prominent features like the u2018electron-ion interaction potentialu2019 are contained in this publication. The same holds true for the somewhat younger but still 35+ year old PhD thesis of I. Cosic on the same topic (2). The roads of Veljkovic and Cosic seem to have diverged. In [this 1985 paper] Veljkovic and Cosic (somewhat) describe their then-unnamed model. In a next [1986 paper] 80248-0) Cosic introduces the model as the u2018Informational Spectrum Methodu2019 (ISM). Veljkovic keeps using this ISM label for the next 35 (!) years, see e.g. a recent [2020 paper] applying the model to the COVID pandemic. Or this [2015 paper] tackling the then-hot H5N1 influenza, a [2015 paper] on an Ebola outbreak, or maybe this [2008 paper] battling anthrax and HIV using the ISM model. Cosic continued using the ISM until 1988, when she rebranded it u2018[Resonant Recognition Model](http: dx.doi.org 10.1109 IEMBS.1988.94709)u2019. Also the RRM is still going strong and is more then 30 years later tackling the COVID pandemic in multiple papers, the [latest from 2022](http: dx.doi.org 10.18483 ijSci.2558). In the intervening years RMM was used to find a cure for many diseases, including [Cystis Fibrosis (2019)](http: dx.doi.org 10.18483 ijSci.2148), [autism (2018)] , [cancer (2017)](http: dx.doi.org 10.22606 jamb.2017.12003), [Crigler-Najjar syndrome (2016)](http: dx.doi.org 10.1140 epjnbp s40366-016-0036-6), [malaria (2015)](http: dx.doi.org 10.1140 epjnbp s40366-015-0025-1), [HIV (2014)] , and [aging (2014)] . The basis of the model is, well, thin and ignores everything that has been learned the past 40 years on protein folding and molecular interactions. The premise is that DNA and proteins are linear molecules that can be described by a linear sequence. For DNA it is the sequence of nuclear acids, for proteins the amino acids. Each acid is assigned a number, the so-called u2018electron-ion interaction potentialu2019 (EIIP). The numerical values for the acids are published in e.g. [this 1989 RMM paper] . One can then take a DNA or protein sequence, convert it into an array of numbers and take the Fourier transform to obtain a frequency spectrum (1 m). The spectrum contains peaks, and the idea is that peaks at a similar frequencies have similar biological functions. Or that molecules with peaks at similar frequencies interact with each other. In [this 1994 paper ] (presenting the then 10 year old model as a u2018new physicomathematical approachu2019) an attempt is made to provide a physical basis. On page 1105 Cosic states: u201c*The process can be observed as the interaction between transmitting and receiving antennae of a radio system. The RRM model assumes that characteristic frequencies are responsible for resonant recognition between macromolecules at a distance. Thus, these frequencies have to represent oscillations of some physical field which can propagate through water dipoles. One possibility is that this field is electromagnetic in nature. There is evidence that proteins and DNA have certain conducting properties. If so, charges would be moving through the backbone of the macromolecule and pass through different energy stages caused by different side groups of various amino acids or nucleotides. This process provides sufficient conditions for the emission of electromagnetic waves. Their frequency range depends on charge velocity, which then depends on the nature of charge movement (superconductive, conductive, soliton transfer, etc.) and on the energy of the field that causes charge transfer. The nature of this physical process is still unknown.*u201d. So it is assumed that electrons move with a constant velocity from one end of the molecule to the other end, on their way bumping over the AGTC acids and emitting (possibly) electromagnetic radiation at frequencies related to the EIIP-encoded sequence. Which then are propagating through water dipoles to interact with neighbouring molecules. In 1994 the author stated that u201c*The nature of this physical process is still unknown*u201d, a statement that still holds true in 2022. There are many things wrong with the model. In the 1994 paper the author stresses the role of the phase of emitted u2018frequenciesu2019 (page 1113): u201c*we can conclude that RRM frequencies, together with their corresponding phases, are important physicochemical parameters that characterize specific biorecognition processes.*u201c. Specifically, opposite phases attract (page 1104): u201c*It has been shown that proteins and their DNA or protein targets share the same characteristic frequency, but of opposite phase for each in a pair of interacting macromolecules.*u201d.When taking a Fourier transform the u2018opposite phaseu2019 can be obtained by reversing the sequence. In other words, the nuclear acid sequences AGCT and TCGA have the same RMM spectral peaks but of opposite phase. Which means that there are not only electrons flying through the DNA molecule, but they only fly from left to right. One could also have a look at the specific EIIP values u2018derivedu2019 in the 1989 RMM paper. With the information provided in the paper it is impossible to reproduce the given numbers. The paper refers to a [1972 article] of (incidentally) Veljkoviu0107 as the basis for their EIIP values. That paper is about an approximate method to derive pseudo-potentials for **metal ions** in a regular lattice. Apparently this method is general enough to reduce the interactions of the presumed fixed-velocity energy electron flying through a nuclear or amino acid *molecule* (non-metal, complex structure) into a single EIPP number. And through a physical process of which the nature is unknown the Fourier transform of the EIPP number sequence correlates with biological function... I want to end this review with a question: [Is there a link between acupuncture meridians, Earth-ionosphere resonances and cerebral activity] ? (1) A Theoretical Approach to the Preselection of Carcinogens and Chemical Carcinogenesis V. Veljkovic Gordon and Breach, Science Publishers, Inc., New York, London and Paris, 1980, 114 pp). (2) Digital Signal Analysis Applied to Protein and Nucleotide Sequences. Cosic I. (1985), Ph. D. Thesis, University of Belgrade ,