  Current methods for viral discovery target evolutionarily conserved proteins
that accurately identify virus families but remain unable to distinguish the
zoonotic potential of newly discovered viruses. Here, we apply an
attention-enhanced long-short-term memory (LSTM) deep neural net classifier to
a highly conserved viral protein target to predict zoonotic potential across
betacoronaviruses. The classifier performs with a 94% accuracy. Analysis and
visualization of attention at the sequence and structure-level features
indicate possible association between important protein-protein interactions
governing viral replication in zoonotic betacoronaviruses and zoonotic
transmission.
