An artificial intelligence method can help combat emerging COVID-19 mutations by identifying the best potential vaccines to treat the virus, according to a study published in Scientific Reports.
As COVID-19 begins to mutate in populations globally, scientists are concerned that the mutations will minimize the effectiveness of vaccines that are now being distributed. Recent variants of the virus in the UK, South Africa, and Brazil seem to spread more easily, which have the potential to lead to more hospitalizations and deaths.
Researchers from the University of Southern California (USC) Viterbi School of Engineering set out to develop a new artificial intelligence method to combat emergent mutations of COVID-19 and accelerate vaccine development.
The team used 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.
IEDB contains over 600,000 known epitopes from some 3,600 different species, along with the Virus Pathogen Resource, a complementary repository of information about pathogenic viruses.
The newly-developed artificial intelligence method is designed to speed the analysis of vaccines and focus on the best potential preventive medical therapy.
The method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identified. This can give humans a significant advantage over evolving mutations, with the model accomplishing vaccine design cycles that once took months or years in a matter of seconds or minutes.
When applied to the virus that causes COVID-19, the AI tool quickly eliminated 95 percent of the compounds that could have possibly treated the pathogens and identified the best options.
The AI-assisted method predicted 26 potential vaccines that would work against coronavirus. Of these, scientists identified the best eleven 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.
“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.”
With the new AI framework, the team can also construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour. In comparison, current processes to control the virus require growing the pathogen in a lab, deactivating it, and injecting the virus that caused a disease. The process can take more than a year as the virus continues to spread.
Researchers expect that if the virus causing COVID-19 becomes uncontrollable by current vaccines, or if new vaccines are needed to deal with other emerging viruses, their AI method can help develop other preventive mechanisms quickly.
For example, researchers in the study used only one B-cell epitope and one T-cell epitope, whereas applying a bigger dataset and more possible combinations could help develop a more comprehensive and faster vaccine design tool.
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.