Recent findings in cancer research has established a connection between a T-antigen - common virus - and a brain tumor in children. The studies suggested the T-antigen, the viral component of a specific virus, called the JC virus, plays a significant role in the development of the most frequent type of malignant brain tumors by blocking the functionality of tumor suppressor proteins such as p53 and pRb. Here we have investigated the structure and function relationships of T-antigen, p53 and pRb proteins using the Resonant Recognition Model (RRM), a physico-mathematical approach based on digital signal processing methods

Akay, M

Cosic, I

Pirogova, E

RMIT Research Repository

Proceedings of the 25' Annual Intemational Conference of the IEEE EMBS 
Cancun, Mexico * September 17-2 1,2003 
Application of the Resonant Recognition Model to Analysis of Interaction 
between Viral and Tumor Suppressor Proteins 
I. Cosic', E. Pirogova', M.Akay2 
'School of Electrical and Computer Engineering, W I T  University, Melbourne, Australia 
'Thayer School of Engineering, Dartmouth College, NH, USA 
Abstract-Recent findings in cancer research has 
established a connection between a T-antigen - common virus 
- and a brain tumor in children. The studies suggested the T- 
antigen, the viral component of a specific virus, called the JC 
virus, plays a significant role in the development of the most 
frequent type of malignant brain tumors by blocking the 
functionality of tumor suppressor proteins such as p53 and 
pRb. Here we have investigated the structure and function 
relationships of T-antigen, p53 and pRb proteins using the 
Resonant Recognition Model (RRM), a physico-mathematical 
approach based on digital signal processing methods. 
Keywords-Signal processing, modeling, protein function, 
and characteristic frequency. 
I. INTRODUCTION 
JC virus (JCV) is a neurotropic polyomavirus infecting 
greater than 70% of the human population worldwide during 
early childhood. JCV possesses an oncogenic potential and 
induces development of various neuroectodermal origin 
tumors including medulloblastomas and glioblastomas. The 
most important role in this process is attributed to T-antigen, 
which has the ability to associate with and functionally 
inactivate well-studied tumor suppressor proteins p53 and 
pRl3. The immunohistochemistry analysis studies revealed 
expression of JCV T-antigen in the nuclei of tumor cells [ 1- 
The RRM [4,5] is a novel engineering approach 
invented for the analysis of protein-protein and protein- 
DNA interactions. The RRM suggests that these bio- 
molecular interactions present the transfer of the resonant 
energy between the interacting molecules at the frequency 
specific for each observed functionlinteraction. This model 
is aimed to find possible patterns of significance and thus 
assists in structure-function studies of different protein 
families. 
The RRM is a physico-mathematical approach that 
interprets protein sequence linear information using digital 
signal processing. In the RRM the protein primary structure 
is represented as a numerical series by assigning to each 
amino acid in the sequence a physical parameter value 
relevant to the protein's biological activity. The RRM 
concept is based on the finding that there is a significant 
correlation between spectra of the numerical presentation of 
amino acids and their biological activity [4,5]. 
It has been found through an extensive research that 
proteins with the same biological function have a common 
frequency in their numerical spectra. This frequency was 
31. 
found then to be a characteristic feature for protein 
biological function or interaction. Once the characteristic 
frequency for a particu1a.r protein function/interaction is 
identified, it is possible th.en to utilize the RRM to predict 
the amino acids in the sequence predominantly contributed 
to this frequency and consequently to the observed function. 
Also it becomes possible to design peptides having the 
desired periodicities [4,5]. 
11. METHODOLOGY 
The RRM, used in thiis study, involves a transformation 
of the amino acid sequence into a numerical sequence and 
then analysis of this sequence by appropriate digital signal 
processing methods (Fourier Transform, wavelets etc.) [7- 
181. To determine the common frequency components in the 
spectra for a group of proteins, the multiple cross-spectral 
function is used. Peaks in this function denote common 
frequency components for all protein sequences analyzed. 
Through an extensive study the RRM has reached a 
fundamental conclusion: one RRM characteristic frequency 
characterizes one particular biological function or 
interaction [4,5]. This frequency is related to the biological 
function provided the following criteria are met: 
0 One peak only exists for a group of protein 
sequences sharing the same biological function. 
0 No significant peak exists for biologically unrelated 
protein sequences. 
0 Peak frequencies are different for different 
biological functions. 
The assignment of a particular number for each amino 
acid in the protein sequence is a crucial step in all these 
calculations. This set of numbers should have a physical 
meaning related to the protein's biological function. In this 
study, the energy of delocalized electrons (calculated as the 
electron-ion interaction pseudo-potential, EIIP [6]) of each 
amino acid residue is ust:d. Thus, the resulting numerical 
series represents the distribution of the free electrons 
energies along the protein. Then, these numerical series are 
analyzed by digital signal analysis methods, in particular by 
Discrete Fourier Transform, in order to extract information 
pertinent to the protein biological function. 
A multiple cross-spectral function is defined and 
calculated to obtain the common frequency components 
from the spectra of a group of proteins. Peaks in such 
function denote commori frequency components for all 
sequences analyzed. Signal-to-noise ratio (S/N) for each 
peak, described a similarity between the analyzed 
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sequences, is calculated as the ratio between the signal 
intensity at the particular peak frequency and the mean value 
over the whole spectrum [4,5]. 
In this study the RRM approach has been used to 
investigate the mutual relationships of brain tumor 
associated JC viral oncoprotein T-antigen and tumor 
suppressor proteins p53 and pRb. 
111. RESULTS 
Over time, cells can accumulate damage to DNA. If the 
right combination of genes becomes altered, the cell will 
become cancerous. Some genes help to prevent malignant 
behavior and are therefore referred to as tumor-suppressor 
genes. These genes contribute to cancer when they are 
inactivated or lost as a result of DNA damage (mutations). 
Other genes, known as proto-oncogenes, can promote cancer 
if they acquire new properties as a result of mutations at 
which point they are called oncogenes. Most common 
cancers involve both inactivation of specific tumor- 
suppressor genes and activation of certain proto-oncogenes. 
The human neurotropic polyomavirus, JCV, produces a 
regulatory protein named T-antigen, which is expressed at 
the early phase of viral lytic infection and plays a critical 
role in completion of the viral life cycle. Furthermore, this 
protein has the ability to transform neural cells in vitro and 
its expression has been detected in several human neural- 
origin tumors. 
JC virus T-antigen most likely infects humans through 
the upper respiratory tract and remains in most people 
throughout their lives, and, in some cases, causes minor sub- 
clinical problems. However, in people whose immune 
systems are depressed, either through chemotherapy given 
to organ transplant recipients or an illness such as AIDS, 
JCV can become active and may contribute to cancer in the 
brain. As it was established the interaction of viral T-antigen 
oncoprotein with p53 and pRb tumor suppressor proteins 
leads to induction of cancer [ 1-31. 
In this study 8-JC viral T-antigen protein sequences, 13- 
p53 protein sequences and 9-pRb protein sequences were 
investigated concerning the understanding of the structure- 
function relationship within these proteins. 
TABLE 1. 
PEAK FREQUNCY AND SIGNAL-TO-NOISE RATIO OF PROTEIN 
GROUPS 
A multiple cross-spectral analysis was performed for 
each selected protein group as well as for their mutual 
combination using the EIIP values (Fig.1-Fig.6). As a result, 
characteristic frequencies of analyzed protein groups were 
obtained and are shown in Tablel. The RRM analysis was 
applied to a group of 8 T-antigen proteins, and the common 
feature in terms of characteristic frequency was identified at 
f=0.2061, S/N=129.58. This frequency component is 
common to all analyzed sequences and therefore can be 
considered as the consensus characteristic of their common 
biological activity of all protein sequences in this functional 
group. 
0 0.1 25 0.25 0.375 0.5 
Fig. 1. Multiple cross-spectral function of JC viral T-antigen 
proteins. The prominent peak(s) denote common frequency 
components. The abscissa represents RRM frequencies, and 
the ordinate is the normalized intensity. 
100 
75 
50 
25 
i 
0.25 0.375 0.5 
Fig. 2. Multiple cross-spectral function of p53 proteins 
The same procedure was repeated with p53 and pRb tumor 
suppressor proteins (Fig.2, Fig.3). The p53 tumor suppressor 
gene has proven to be one of the genes most often mutated 
in human cancers. It involves mainly point mutations 
leading to amino acid substitutions in the central region of 
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75 loo t J 
0 0.1 25 0.25 0.375 0.5 
Fig. 3. Multiple cross-spectral function of pRb proteins 
0.25 0.375 0.5 
Fig. 4. Multiple cross-spectral finction of JC viral T-antigen 
and p53 proteins 
100 
75 
50 
25 
0 
Fig. 5. 
0 0.1 25 0.25 0.375 0.5 
Multiple cross-spectral function of JC viral T-antigen 
and pRb proteins 
0 0.1 25 0.25 0.375 0.5 
Fig. 6. Multiple cross-spectral function of T-antigen, p53 
and pRb proteins 
the protein and thus, causes to its abnormal functions. The 
prominent characteristic fiequencies of p53 and pRb tumor 
suppressor proteins were identified at e0.4326, S/N=159.97 
and at 60.4316, ShJz.164.28 respectively. As was 
mentioned above each specific biological function of the 
protein is characterized by a single frequency. The similarity 
of characteristic frequencies of p53 and pRb proteins (Table 
1) is expected as both p53 and pRb proteins are tumor 
suppressors sharing the same biological function. Thus, the 
frequency 60.4326 identified within the RRM analysis is 
considered as a characteristic feature of the specific 
biological activity of p53 ,md pRB proteins - ability to stop 
the formation of tumors. After careful examining of the 
corresponding consensus spectrums of p53 and pRb proteins 
(Fig. 2, Fig. 3) we observe more than one (less significant) 
peaks corresponding to other biological functions 
determined within the Wvl analysis. 
IV. DISCUSSION 
It is proposed that the RRM characteristic frequencies 
present the common feature of the interacting sequences and 
thus, a common interaction. In our previous work it was also 
proposed that this characteristic frequency could represent 
the oscillations of a physical field, which are responsible for 
information transfer between the interacting bio-molecules 
[7-191. As a consequence, it is postulated that RRM 
characteristic frequency i:; a relevant parameter for mutual 
recognition between bio-molecules and is significant in 
describing the interaction between proteins and their 
substrates or targets. Th’erefore, it is concluded that the 
RRM characteristic frequency may dictate the specificity of 
the protein interactions. 
From Fig.4 we can observe only one dominant peak 
corresponding to the common frequency component for the 
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combined group of T-antigen and p53 proteins at f=0.2021, 
S/N=3 12.36. Analogous results were obtained in the 
analysis of interactions between T-antigen and pRb proteins. 
A single peak corresponding to the protein’s biological 
activity was identified at +0.2041, S/N=167.12. These 
characteristic frequencies are very close to each other (Table 
1) and they overlap with each other within the calculation 
error, indicating their mutual recognition. Therefore, we can 
conclude that this identified frequency can be considered as 
characteristic feature of the mutual interactions between the 
analyzed proteins T-antigen and p53, and T-antigen and pRb 
respectively causing the cell damage and induction of tumor 
formation in brain. 
Finally, we have explored the possibility of mutual 3- 
components interaction between T-antigen, p53 and pRb 
proteins by applying the RRM cross-spectral analysis to all 
three functional groups of proteins. Interestingly, we have 
found that there is a very prominent frequency component 
(Fig. 6) at f-0.2021, U”506.28 (Table 1) shared by all 
analyzed sequences. It should be noted that this frequency is 
the same (within the calculation error) as the characteristic 
frequency of T-antigen and pRb proteins (Fig. 5) and of T- 
antigen and p53 proteins respectively. Thus, the results 
obtained indicate the common characteristic frequency for 
all three interacting proteins: T-antigen, p53 and pRb 
proteins at f=0.202 1, which is a characteristic feature of the 
tumor formation. This confirms the RRM main concept that 
proteins and their targets recognizehnteract with each other 
based on the same (similar) characteristic frequency. 
Consequently, we conclude that 3-components interaction 
between T-antigen, p53 and pRb proteins can be considered 
as a crucial condition in the process of the brain tumor 
formation. 
V. CONCLUSION 
We have shown previously that the digital signal 
processing methods can be used to analyze linear sequences 
of amino acids to reveal the informational content of 
proteins [7-191. This study extends the utility of the RRM 
procedures, to viral T-antigen oncoprotein and tumor 
suppressor p53, pRb proteins. 
The results of our computational analysis clearly 
indicate that the RRM can determine the protein 
characteristic frequencies crucial for biological 
activity/interaction of analyzed proteins. Moreover, our 
successful investigations [7-181 have proved that the RRM 
presents an engineering tool based on digital signal 
processing that is able to identify the protein characteristic 
patterns in protein sequences related to the common 
biological functionlinteraction of studied proteins. And 
consequently, knowing the protein characteristic frequency 
allow us to allocate the protein’s biological active site(s), 
and design new peptides with the desired biological function 
[12,13]. This novel prediction scheme can be used to 
facilitate the structure-function studies under different 
protein families and thus, save the experimental cost greatly. 
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Application of the resonant recognition model to analysis of interaction between viral and tumor suppressor proteins

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