A model
that predicts levels of coronavirus (CoV) respiratory and
fecal–oral transmission potentials based on the shell disorder
has been built using neural network (artificial intelligence, AI)
analysis of the percentage of disorder (PID) in the nucleocapsid,
N, and membrane, M, proteins of the inner and outer viral shells,
respectively. Using primarily the PID of N, SARS-CoV-2 is grouped
as having intermediate levels of both respiratory and fecal–oral
transmission potentials. Related studies, using similar methodologies,
have found strong positive correlations between virulence and inner
shell disorder among numerous viruses, including Nipah, Ebola, and
Dengue viruses. There is some evidence that this is also true for
SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and case-fatality
rates of 0.5–5% and 10.9%, respectively. The underlying relationship
between virulence and respiratory potentials has to do with the viral
loads of vital organs and body fluids, respectively. Viruses can spread
by respiratory means only if the viral loads in saliva and mucus exceed
certain minima. Similarly, a patient is likelier to die when the viral
load overwhelms vital organs. Greater disorder in inner shell proteins
has been known to play important roles in the rapid replication of
viruses by enhancing the efficiency pertaining to protein–protein/DNA/RNA/lipid
bindings. This paper suggests a novel strategy in attenuating viruses
involving comparison of disorder patterns of inner shells (N) of related
viruses to identify residues and regions that could be ideal for mutation.
The M protein of SARS-CoV-2 has one of the lowest M PID values (6%)
in its family, and therefore, this virus has one of the hardest outer
shells, which makes it resistant to antimicrobial enzymes in body
fluid. While this is likely responsible for its greater contagiousness,
the risks of creating an attenuated virus with a more disordered M
are discussed.