  Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA
viruses belonging to the Coronaviridae family. Various animal species, mainly
mammalian and avian, are severely infected by various coronaviruses, causing
serious concerns like the recent pandemic (COVID-19). Therefore, building a
deeper understanding of these viruses is essential to devise prevention and
mitigation mechanisms. In the Coronavirus genome, an essential structural
region is the spike region, and it's responsible for attaching the virus to the
host cell membrane. Therefore, the usage of only the spike protein, instead of
the full genome, provides most of the essential information for performing
analyses such as host classification. In this paper, we propose a novel method
for predicting the host specificity of coronaviruses by analyzing spike protein
sequences from different viral subgenera and species. Our method involves using
the Poisson correction distance to generate a distance matrix, followed by
using a radial basis function (RBF) kernel and kernel principal component
analysis (PCA) to generate a low-dimensional embedding. Finally, we apply
classification algorithms to the low-dimensional embedding to generate the
resulting predictions of the host specificity of coronaviruses. We provide
theoretical proofs for the non-negativity, symmetry, and triangle inequality
properties of the Poisson correction distance metric, which are important
properties in a machine-learning setting. By encoding the spike protein
structure and sequences using this comprehensive approach, we aim to uncover
hidden patterns in the biological sequences to make accurate predictions about
host specificity. Finally, our classification results illustrate that our
method can achieve higher predictive accuracy and improve performance over
existing baselines.

Quantitative Methods;Machine Learning