One of the complications in the battle against COVID has been the rise in recent months of a growing array of mutations of the virus, but a recently released study may have identified at least one potential solution.

For the study, published in Scientific Reports, researchers from the University of Southern California (USC) Viterbi School of Engineering set out to develop a new AI method to combat emergent mutations of COVID-19 and accelerate vaccine development. 

Tapping data from a bioinformatics database called the Immune Epitope Database (IEDB), in which scientists around the world have been collecting data about the coronavirus and other diseases, the team developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.

“This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety,” said Paul Bogdan, associate professor of electrical and computer engineering at USC Viterbi and corresponding author of the study. “Moreover, this can be adapted to help us stay ahead of the coronavirus as it mutates around the world.”

According to the study, when applied to SARS-CoV-2 — the virus that causes COVID-19 — the computer model quickly eliminated 95% of the compounds that could’ve possibly treated the pathogen and pinpointed the best options. The AI-assisted method predicted 26 potential vaccines that would work against the coronavirus. From those, the scientists identified the best 11 from which to construct a multi-epitope vaccine, which can attack the spike proteins that the coronavirus uses to bind and penetrate a host cell. Vaccines target the region — or epitope — of the contagion to disrupt the spike protein, neutralizing the ability of the virus to replicate.

In addition, the engineers can construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour. By contrast, current processes to control the virus require growing the pathogen in the lab, deactivating it and injecting the virus that caused a disease. The process is time-consuming and takes more than one year; meanwhile, the disease spreads.

The study estimates that the model can perform accurate predictions with more than 700,000 different proteins in the dataset.

“The proposed vaccine design framework can tackle the three most frequently observed mutations and be extended to deal with other potentially unknown mutations,” Bogdan said.