Machine learning for the identification of respiratory viral attachment machinery from sequences data.

ABSTRACT: At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest mod

SUBMITTER: Walker KC 

PROVIDER: S-EPMC9980812 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature