Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning

TL;DR

This paper introduces a deep learning framework called Optimus PPIme to predict highly infectious future virus variants, aiding preemptive vaccine development and pandemic prevention.

Contribution

It presents a novel deep learning approach combining mutation algorithms and a transformer network to forecast more infectious virus variants before they emerge.

Findings

Transformer network achieved 90% accuracy in binding strength prediction.

Beam search identified more infectious variants than greedy search.

The method can potentially guide vaccine design against future variants.

Abstract

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.