During natural evolution, influenza viral proteins can mutate to gain new properties that help them survive within and spread to different hosts. The massive surveillance data on influenza viral sequences allow us to learn from natural selection by mapping viral genomic sequences to protein functions using machine learning. In this work, we demonstrate a novel machine learning classifier capable of predicting key influenza viral protein functions, trained solely on naturally occurring (positive-only) influenza sequences. The accuracy of our model is first verified using site-directed mutagenesis data from the literature and further supported by our experimental results. By integrating machine learning with domain-specific biochemistry knowledge of protein evolution, our work shows great promise in predicting viral variants of concern for disease surveillance and in assisting protein design for disease prevention and therapeutics.
