  COVID-19 pandemic, is still unknown and is an important open question. There
are speculations that bats are a possible origin. Likewise, there are many
closely related (corona-) viruses, such as SARS, which was found to be
transmitted through civets. The study of the different hosts which can be
potential carriers and transmitters of deadly viruses to humans is crucial to
understanding, mitigating and preventing current and future pandemics. In
coronaviruses, the surface (S) protein, or spike protein, is an important part
of determining host specificity since it is the point of contact between the
virus and the host cell membrane. In this paper, we classify the hosts of over
five thousand coronaviruses from their spike protein sequences, segregating
them into clusters of distinct hosts among avians, bats, camels, swines, humans
and weasels, to name a few. We propose a feature embedding based on the
well-known position-weight matrix (PWM), which we call PWM2Vec, and use to
generate feature vectors from the spike protein sequences of these
coronaviruses. While our embedding is inspired by the success of PWMs in
biological applications such as determining protein function, or identifying
transcription factor binding sites, we are the first (to the best of our
knowledge) to use PWMs in the context of host classification from viral
sequences to generate a fixed-length feature vector representation. The results
on the real world data show that in using PWM2Vec, we are able to perform
comparably well as compared to baseline models. We also measure the importance
of different amino acids using information gain to show the amino acids which
are important for predicting the host of a given coronavirus.
