  More infectious virus variants can arise from rapid mutations in their
proteins, creating new infection waves. These variants can evade one's immune
system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to
improve vaccine design, this project proposes Optimus PPIme - a deep learning
approach to predict future, more infectious variants from an existing virus
(exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as
a "virus" attacking a host cell. To increase infectivity, the "virus" mutates
to bind better to the host's receptor. 2 algorithms were attempted - greedy
search and beam search. The strength of this variant-host binding was then
assessed by a transformer network we developed, with a high accuracy of 90%.
With both components, beam search eventually proposed more infectious variants.
Therefore, this approach can potentially enable researchers to develop vaccines
that provide protection against future infectious variants before they emerge,
pre-empting outbreaks and saving lives.
