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Possibility to Interfere with Coronavirus RNA Replication Analyzed by Resonant Recognition Model

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To be able to design vaccine or even a cure for COVID-19, it is particularly important to understand how SARS-CoV-2, as a single stranded RNA virus, is multiplied within host cells and which factors are controlling this multiplication. Here, we have analyzed the process of coronavirus RNA replication within host cell with the aim to find out the characteristics of this process. For that purpose, we have utilized the Resonant Recognition Model (RRM), which is biophysical model capable of identifying parameters (frequencies) related to specific macromolecular (protein, DNA, RNA) functions and/or interactions. The RRM model is unique with its capability to directly analyze interactions between amino acid macromolecules (proteins) and nucleotide macromolecules (DNA, RNA). Using the RRM model, we have identified parameters that characterize two steps in coronavirus RNA replication i.e., initiation of replication and replication by itself. These parameters can be used in our future research to design peptides, that will be able to interfere with either or both of those processes.
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Irena Cosic (Correspondence)
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Published at: http://www.ijsciences.com/pub/issue/2021-06/
DOI: 10.18483/ijSci.2482; Online ISSN: 2305-3925; Print ISSN: 2410-4477
Possibility to Interfere with Coronavirus RNA
Replication Analyzed by Resonant
Recognition Model
Irena Cosic1, Drasko Cosic1, Ivan Loncarevic2
1AMALNA Consulting, Black Rock, 3193, Australia
2QuantBioRes - QBR A/S, Copenhagen, 2860, Denmark
Running Title: Analyzes of Coronavirus Viral RNA Replication
Abstract: To be able to design vaccine or even a cure for COVID-19, it is particularly important to understand how SARS-CoV-2, as a single
stranded RNA virus, is multiplied within host cells and which factors are controlling this multiplication. Here, we have analyzed the process of
coronavirus RNA replication within host cell with the aim to find out the characteristics of this process. For that purpose, we have utilized the
Resonant Recognition Model (RRM), which is biophysical model capable of identifying parameters (frequencies) related to specific
macromolecular (protein, DNA, RNA) functions and/or interactions. The RRM model is unique with its capability to directly analyze interactions
between amino acid macromolecules (proteins) and nucleotide macromolecules (DNA, RNA). Using the RRM model, we have identified
parameters that characterize two steps in coronavirus RNA replication i.e., initiation of replication and replication by itself. These parameters can
be used in our future research to design peptides, that will be able to interfere with either or both of those processes.
Keywords: COVID-19, SARS-CoV-2, Coronavirus, Coronavirus RNA Replication, Resonant Recognition Model
Introduction
Current COVID-19 pandemic is caused by SARS-
CoV-2 virus, which belongs to group of single-
stranded RNA viruses, more specifically
coronaviruses. Coronaviruses are widely spread in
nature, mostly infecting animals, but some can infect
humans as well, usually with mild or non-existent
symptoms [1]. However, there have been so far three
instances where coronaviruses had infected humans
causing severe symptoms, including SARS (Severe
Acute Respiratory Syndrome, 2003), MERS (Middle
East Respiratory Syndrome, 2012) and COVID-19
(2019-nCoV or SARS-CoV-2, 2019). While SARS
and MERS outbreaks were within relatively limited
areas of population in China and Middle East
respectively, SARS-CoV-2 caused worldwide
pandemic. In addition, SARS and MERS have been
less infectious, but with higher mortality rate, while
coronavirus SARS-CoV-2 is more infectious, but
with lower mortality rate [2].
With the worldwide COVID-19 pandemic there is
question if it is possible to design coronavirus
vaccine or even cure. Firstly, it is particularly
important to understand how SARS-CoV-2, as
single-stranded RNA virus, is multiplied within host
cells and which factors are controlling this
multiplication. Infection begins when the viral spike
protein attaches to its complementary host cell
receptor. After attachment, a protease of the host cell
cleaves and activates the receptor-attached spike
protein. Depending on the host cell protease available,
this cleavage and activation process allows the virus
to enter the host cell [3].
On entry into the host cell, the virus particle is
uncoated, and its genome enters the cell cytoplasm
[4]. The coronavirus RNA genome has a 5′
methylated cap and a 3′ polyadenylated tail, which
allows viral RNA to attach to the host cell's ribosome
for translation [4]. The host ribosome translates the
initial overlapping open reading frame of the virus
genome and forms a long polyprotein. The
polyprotein has its own proteases, which cleave the
polyprotein into multiple non-structural proteins [4].
Thus, there are two points to attack the virus, either to
prevent its entry into the host cell, or to prevent its
replication within the host cell. Here, we will
concentrate to prevent replication within the host cell.
Once, when the virus has entered the host cell, the
most appropriate chance to prevent and find cure for
the further infection is to prevent viral RNA
translation. It is well known for initiation of RNA
replication 5‘ and 3‘ noncoding parts of RNA could
be critical in controlling RNA replication, as pointed
out in ref [5]: ―the nucleotide sequence of the 5′ non-
coding region of messenger RNAs is particularly
interesting because it may contain the signals for
processing and modification of mRNA, such as its
cleavage from precursor and ‗capping‘ (the addition
of the 5′ terminal m7Gppp) and possible recognition
and binding sites for ribosomes and initiation factors‖.
Thus, we propose that interfering with RNA
replication regulation through 5‘ noncoding segment
could be possible way of preventing RNA virus
replication.
Activation of 5‘ and 3‘ noncoding control segments
of RNA is done by binding with specific RNA related
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proteins. One possible approach to understand this
interaction process is to investigate function of
argonaute proteins which are key players in small-
RNA-guided gene regulation. Argonaute proteins are
highly specialized small-RNA-binding proteins that
coordinate downstream gene-silencing events and
thus are considered to be the key components of
RNA silencing pathways [6]. Recent work has made
progress in understanding of argonaute-mediated
gene-silencing principles, such as the effects on
mRNA translation and decay, but has also implicated
the argonaute proteins in several other cellular
processes, such as transcriptional regulation and
splicing [7]. In summary, argonaute proteins which
consist of groups: AGO1, AGO2, AGO3 and AGO4,
are highly specialized small-RNA-binding proteins
driven by small RNAs to complementary target
mRNAs, where they act together with protein binding
partners to interfere with translation of target mRNAs.
During virus infection individual functions of the
mammalian antiviral RNA interference (RNAi) and
micro-RNA (miRNA) effector proteins like
argonautes (AGO1AGO4) have been investigated
showing that AGO4 has uniquely antiviral function in
mammalian cells and has evolved to obtain antiviral
RNAi [8].
In addition, there are number of RNA-dependent
RNA polymerases that use RNA as their template for
synthesis of a new strand of RNA. For instance,
number of RNA viruses (such as poliovirus) use this
type of enzyme to replicate their genetic material [9].
Also, RNA-dependent RNA polymerase is part of the
RNA interference pathway in many organisms [10].
Of particular interest is viral replicase as well, which
is enzyme that coordinates and mediates process of
RNA synthesis [11].
On the other hand, there is number of 294 antibodies
against SARS-CoV-2 mostly neutralizing SARS-
CoV-2 by blocking its binding to ACE2 receptor,
where the most prevalent of those antibodies is
IGHF3-53 [12]. Besides the fact that IGHF3-53
antibody can prevent viral spike protein interaction
with ACE2 receptor, we will investigate here its
possible interaction with RNA regulatory 5‘ segment
and its possible role in preventing viral replication.
Baring all this in mind, we propose that interfering
with RNA replication regulation through RNA
directed transcriptase and viral replicase or through 5‘
signal sequences within non-coding region of mRNA
could be possible way of preventing RNA virus
replication. Here, we have utilized our own
biophysical Resonant Recognition Model (RRM)
[13-20], which is capable of directly analyzing
interaction between proteins and RNA [21-22], to
analyze interaction between argonaute proteins,
antibody IGHF3-53 and related RNA regulatory 5‘
signal sequences within non-coding region of mRNA
with the aim to find out characteristic frequencies for
initiation of viral replication. In addition, we have
analyzed RNA replication through RNA directed
transcriptase and viral replicase to identify
characteristics of RNA viral replication by itself.
Once when these RRM characteristics are identified,
it is possible to further utilize the RRM model to
design peptides, which would be able to interfere
with initiation of viral replication and/or directly
interfere with viral replication to possibly prevent
virus multiplication within the host cell.
Methods
Resonant Recognition Model
The Resonant Recognition Model (RRM) is
biophysical, theoretical model that can analyze
interactions between proteins and their targets, which
could be other proteins, DNA, RNA, or small
molecules. The RRM has been previously published
in detail within number of publications [13-20]. The
RRM model is based on the findings that certain
periodicities (frequencies) within the distribution of
energy of delocalized electrons along protein
backbone are critical for protein biological function
and/or interaction with their targets. The distribution
of delocalized electrons energies is calculated by
assigning each amino acid specific physical
parameter representing the energy of delocalized
electrons of each amino acid. Consequently, the
spectral characteristics of such energy distribution
(signal) are calculated using Fourier Transform. This
means that the linear numerical signal representing
the distribution of energies along the protein is
transformed into the frequency domain and is
characterized by number of different frequencies
containing all information from the original signal.
Comparing such spectra using cross-spectral function
for proteins, which are sharing the same biological
function/interaction, it has been shown that they share
the same frequency within the spectrum of free
energy distribution along the macromolecule [13-20].
Peak frequencies in such multiple cross-spectral
function present common frequency components for
all macromolecular sequences compared. The
comprehensive analysis done so far confirms that all
macromolecular sequences, with the common
biological function and/or interaction, have common
frequency component, which is specific feature for
the observed biological function/interaction [13-20].
Thus, each specific macromolecular biological
function/interaction within macromolecule is
characterized by specific RRM frequency.
Each biological function is driven by proteins that
selectively interact with other proteins, DNA/RNA
regulatory segments or small molecules. Through
extensive use of RRM model, it has been shown that
proteins and their targets share the same matching
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RRM characteristic frequency [13-24]. The matching
of periodicities within the distribution of energies of
free electrons along the interacting proteins can be
regarded as the resonant recognition and as such is
highly selective. Thus, the RRM frequencies
characterize not only protein function, but also
recognition and interaction between protein and its
targets: proteins (receptors, binding proteins, and
inhibitors), DNA/RNA regulatory segments or small
molecules. In addition, it has been also shown that
interacting macromolecules have opposite phases at
their characteristic RRM recognition frequency [13-
20]. Every frequency can be presented by one
sinusoid characterized with its three parameters:
frequency, amplitude, and phase. The phase is
presented in radians and can be between –π and +π (-
3.14 and +3.14). The phase difference of or about π
(3.14) is considered to be opposite phase. The phase
value can be presented in the phase circle where it is
visually easier to observe phase differences.
The RRM model is unique approach, where it is
possible to analyze interactions directly
computationally between amino acid sequences
(proteins) and nucleotide sequences (DNA and RNA),
based only on matching frequencies within free
electron energy distribution along these
macromolecules. However, for the comparison of
characteristic frequencies between proteins and
DNA/RNA macromolecules, it is required to adjust
for the difference in distances between amino acids
(3.8Å) and nucleotides (3.4Å) along the
macromolecular backbone. These adjustments are
made on nucleotide sequences spectrum, so the result
could be compared with frequency calculations made
for the proteins. These calculations enable the RRM
to be the unique model capable of analyzing and
directly comparing activities and interactions of
proteins, DNA and RNA by identifying their
characteristic frequencies, as tested, and described in
number of previous publications [13-16,21-22].
Once the characteristic frequency for biological
function and/or interaction of the macromolecule is
identified, it is possible to design new
peptides/proteins with the desired RRM frequency
components and consequently with desired biological
functions and/or interactions [13-16,25-30]. This
design approach has already been successfully
applied and experimentally tested in the design of
FGF analogue [25], HIV envelope protein analogue
[26-28] and peptide to mimic myxoma virus
oncolytic function [29-30].
As viruses are mutating their proteins, DNA and/or
RNA very quickly, it is extremely hard to design
general cure or vaccine. This explains why current
approaches based on macromolecular homology are
not successful enough for different viral strains.
However, even when viruses are mutating so often
and so quickly, they will still keep their specific
functionality. It is important to understand that the
RRM model is particularly efficient when it is
applied to viruses, because the RRM model identifies
the common characteristic parameter(s) for all
mutated viruses, related to specific common virus
protein‘s biological function. Thus, the RRM analysis
of viruses does not depend on virus mutations, as
long as they keep their functionality. This RRM
approach has been experimentally tested on the
example of HIV virus, which is continuously
mutating, but all isolates keep the same functionality,
as well as common RRM characteristic frequency
[26-28].
Results
Here, we have focused on analysis of coronavirus
RNA virus replication after virus enters the host cell.
At that moment protease of the host cell cleaves and
activates spike protein and the complete viral RNA
becomes uncoated with its RNA genome entering the
cell cytoplasm [4]. This is the critical point for further
viral multiplication within the host cell. If it is
possible to interfere at this critical point to disable
viral multiplication it would prevent the development
of related disease. The viral multiplication can be
prevented by either interfering with initiation of viral
RNA replication or directly by interfering viral RNA
replication by itself.
Signal sequence within 5‘ non-coding region of
mRNA is particularly interesting for initiation of viral
RNA replication [5]. When we have analyzed 5‘
signal sequences within non-coding region of viral
mRNA from different SARS coronavirus isolates,
from UniProt database (MN938384.1, MN975262.1,
MN985325, MN988668.1, MN988713, MN994467,
MN994468, MN997409, MT072688.1,
NC_045512.2), using the RRM, the most prominent
common RRM frequency has been found at
f1=0.0234±0.0043, as presented in Figure 1.
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Figure 1. RRM cross-spectrum of 5‘ signal sequences within non-coding region of viral mRNA different SARS coronavirus isolates with
common characteristic frequency at f1=0.0234±0.0043.
This common frequency f1 could be proposed to
characterize functionality of 5‘ signal sequence,
which allows viral RNA to attach to the host cell
ribosome and is critical for initiation and control of
viral RNA replication [5].
In addition, as it has been pointed out in Introduction,
argonaute proteins (AGO1, AGO2, AGO3, AGO4)
have a roll in mRNA translation, transcriptional
regulation, and splicing [6-7]. In particular, AGO4
protein has uniquely antiviral function in mammalian
cells [8]. Thus, we propose that argonaute (AGO4)
protein function is related to activation of 5‘ signal
sequences within non-coding region of viral mRNA.
When we compared 5‘ signal sequences within non-
coding region of viral mRNA from different SARS
coronavirus isolates with mammalian AGO4 proteins,
from UniProt database (Q9ZVD5, Q5ZMW0,
Q9HCK5, Q8CJF8, Q9SDG8, Q0JF58, Q4KLV6),
using the RRM, the same even more prominent
common RRM frequency has been found at
f1=0.0234±0.0043, as presented in Figure 2.
Figure 2. RRM cross-spectrum of 5‘ signal sequences within non-coding region of viral mRNA from different SARS coronavirus isolates and
mammalian AGO4 proteins with common characteristic frequency at f1=0.0234±0.0043.
It is interesting to note that there is one unique
common characteristic frequency for 5‘ signal
sequences within non-coding region of viral mRNA
and argonaute (AGO4) proteins indicating the
possibility that initiation of viral RNA replication
involves host AGO4 proteins and viral 5‘ signal
sequences within non-coding region of viral mRNA.
On the other hand, as explained in Introduction, there
is large number of antibodies against SARS-CoV-2,
where the most prevalent one is IGHF3-53 [12].
Besides the fact that IGHF3-53 antibody can prevent
viral spike protein interaction with ACE2 receptor,
we have investigated here possible interaction of
IGHF3-53 antibody with viral RNA regulatory 5‘
signal sequences within non-coding region of viral
mRNA as possible pathway for preventing initiation
of viral replication.
When we have compared IGHF3-53 antibody with
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previously analyzed 5‘ signal sequences within non-
coding region of viral mRNA different SARS
coronavirus isolates and mammalian AGO4 proteins,
from UniProt database (P01767), using the RRM, the
same RRM frequency of f1=0.0234±0.0043, became
even more prominent, as presented in Figure 3.
Figure 3. RRM cross-spectrum of IGHF3-53 antibody, 5‘ signal sequences within non-coding region of viral mRNA different SARS coronavirus
isolates and mammalian AGO4 proteins with common characteristic frequency at f1=0.0234±0.0043.
This result indicates that IGHF3-53 antibody may be
also able to interfere with activation of viral 5‘ signal
sequences within non-coding region of viral mRNA
and mimic activity of host AGO4 proteins, and thus
prevent initialization of viral RNA replication. This
result can be significant as we can use RRM
frequency f1=0.0234±0.0043 as the basis to design
peptides that can prevent initialization of viral RNA
replication.
Another way to interfere with viral RNA replication
is to prevent RNA dependent RNA polymerase
and/or RNA replicase activity, which is critical for
replication of viral RNA [9-11]. When we have
analyzed RNA dependent RNA polymerase and RNA
replicase, from UniProt database (Q02382, P25328,
Q07048 and P35928, P20126, P36304, P20127,
P10358, P20128, P28477, P59595), using the RRM,
the most prominent RRM frequency was identified at
f2=0.0713±0.0011, as presented in Figure 4.
Figure 4. RRM cross-spectrum of RNA dependent RNA polymerase and RNA replicase proteins with common characteristic frequency at
f2=0.0713±0.0011.
This frequency f2=0.0713±0.0011 is characterizing
RNA dependent RNA polymerase and RNA replicase
activity, i.e., replication of viral RNA.
Baring all above in mind, we propose that by
interfering with frequency f1, we can prevent
initiation of viral RNA replication or by interfering
with frequency f2, we can prevent viral RNA
replication by itself. Using either or both of those
ways, we propose that it would be possible to develop
cure for coronaviruses. One possibility is to design
peptides, using RRM model, that will be able to
interfere with either or both of those activities.
To achieve this, we have firstly identified two RRM
characteristic frequencies: f1=0.0234+0.0043
characterizing initiation of viral RNA replication and
f2=0.0713±0.0011 characterizing viral RNA
replication by itself. As described in RRM
methodology, it is possible to design peptides having
only the characteristic frequency and phase related to
targeted protein. Thus, we have identified phases for
all participants involved in initiation of RNA
replication, as presented in Figure 5, within phase
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circle: for 5‘ signal sequences within non-coding
region of viral mRNA from different SARS
coronavirus isolates with phase of -1.74rad in green,
for IGHF3-53 antibody with phase of +2.53rad in
blue and for human AGO4 protein with phase of
+2.43rad in red. It can be easily observed from Figure
5, that phases for human AGO4 and IGFH3-53
antibody are very close to each other and almost
opposite to phase for 5‘ signal sequences within non-
coding region of viral mRNA from different SARS
coronavirus isolates indicating that both AGO4 and
IGHF3-53 interact with 5‘ signal sequences within
non-coding region of viral mRNA during the process
of initiation of RNA replication.
Figure 5. Phase circle at frequency of f1=0.0234 for 5‘ signal
sequences within non-coding region of viral mRNA from different
SARS coronavirus isolates with phase of -1.74rad in green, for
IGHF3-53 antibody with phase of +2.53rad in blue and for human
AGO4 protein with phase of +2.43rad in red. It can be easily
observed that phases for human AGO4 and IGFH3-53 antibody are
very close to each other and almost opposite to phase for 5‘ signal
sequences within non-coding region of viral mRNA from different
SARS coronavirus isolates indicating that both AGO4 and IGHF3-
53 interact with 5‘ signal sequence during the process of initiation
of RNA replication.
In similar fashion, the phase for characteristic RRM
frequency f2 has been identified to be at +2.11rad.
Having identified the characteristic frequency f1 for
initiation of RNA replication and frequency f2
characterizing viral RNA replication, as well as
relevant phases at these frequencies, it will be
possible to design peptides that can interfere with one
and/or the other process in RNA replication and thus
could be considered as possible cure for number of
coronavirus related diseases.
Discussion and Conclusion
Here, we have analyzed the process of coronavirus
RNA replication within host cell with the aim to
find out the characteristics of this process. These
characteristics can be used further in design of
peptides possibly capable to interfere with this
process and prevent viral RNA replication within
host cell. For that purpose, we have utilized the
Resonant Recognition Model (RRM), which is
biophysical model capable of identifying
parameters (frequencies) related to specific
macromolecular (protein, DNA, RNA) function
and/or interaction. The RRM model is unique with
its capability to directly analyze interactions
between amino acid macromolecules (proteins)
and nucleotide macromolecules (DNA, RNA).
Using the RRM model, we have identified
parameters that characterize two steps in RNA
replication i.e., initiation of replication and
replication by itself. Within the first step, we have
identified RRM frequency f1=0.0234±0.0043,
which is common for all 5‘ signal sequences
within non-coding region of viral mRNA different
SARS coronavirus isolates. As these 5‘ signal
sequences are critical for initiation of viral RNA
replication, we propose that this frequency f1 is
characterizing initiation of viral RNA replication.
In addition, we have found that the same
frequency f1 is common with host‘s argonaute
AGO4 proteins, which are key components of
RNA silencing pathways [6] and thus we propose
the possibility that negative control of initiation of
viral RNA replication involves host‘s AGO4
proteins. We have also found that IGHF3-53
antibody shares the same common frequency f1
with 5‘ signal sequences within non-coding region
of viral mRNA different SARS coronavirus
isolates and mammalian AGO4 proteins and thus
we propose that IGHF3-53 antibody also have a
role in negative control of initiation of viral RNA
replication. As both AGO4 and IGHF3-53
antibody are proteins involved in negative control
of initiation of viral RNA replication, sharing the
same RRM characteristic frequency f1 with 5‘
non-coding signal sequence, but with opposite
phase, is confirming that frequency f1 is critical
for negative control of initiation of viral RNA
replication within host cell.
Within the second step of our analysis, we have
identified RRM frequency f2=0.0713±0.0011,
which is common to RNA dependent RNA
polymerase and RNA replicase, and thus is
proposed to characterize the process of viral RNA
replication by itself.
Having identified the characteristic frequency
f1=0.0234±0.0043 for initiation of RNA
replication and frequency f2=0.0713±0.0011
characterizing viral RNA replication, we propose
that by interfering with frequency f1, we can
prevent initiation of viral RNA replication and by
interfering with frequency f2, we can prevent viral
RNA replication. One possible approach would be
to design peptides, using the RRM model, that will
be able to interfere with either or both of these
processes. This design will be a matter of our
future research.
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Contributions
Conceptualization, I.C.; Methodology, I.C. and
D.C.; Software, D.C.; Resources I.L.; Writing
Original Draft PreparationReview and Editing,
I.C., D.C. and I.L.
Competing Interests
Authors declare they have no competing interests.
Human/Animal Involvement
Authors declare that there were no human
participants nor any animal involvement in this
study.
Funding
This research received no external funding.
Acknowledgement
The authors would like to thank Miss Amy Cosic
for proofreading this manuscript.
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