A group of researchers made mostly of engineers have used Artificial Intelligence (AI) to develop a novel method that could tame the emergence of new strains of COVID-19 virus.

The new research by the University of South Carolina (USC) School of Engineering team is among the first few of such targeted at providing a solution to the mutation of SARS-CoV-2—the virus that causes COVID-19 that could hasten vaccine development.

The method, according to a publication by the USC speeds the analysis of vaccines thereby allowing vaccine developers to zero in on the best potential preventive medical therapy. It is easily adaptable to analyze potential mutations of the virus, making it easy for the best possible vaccines to be quickly identified.

The raw data for the research comes from a giant bioinformatics database called the Immune Epitope Database (IEDB) in which scientists around the world have been compiling data about the coronavirus, among 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.

In their study which appeared in Nature Research’s Scientific Reports, the researchers are hopeful that this solution would give humans a big advantage over the evolving infection, as their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes.

When applied to SARS-CoV-2, the computer model quickly eliminated 95% of the compounds that could’ve possibly treated the pathogen and pinpointed the best options.

“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,” Paul Bogdan, Associate Professor of electrical and computer engineering at USC Viterbi and corresponding author of the study said.

He said this can be adapted to help humans stay ahead of the coronavirus as it mutates around the world.

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.

With the understanding that vaccines target the region (epitope) of the contagion to disrupt the spike protein, neutralizing the ability of the virus to replicate, the engineers say they can construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour.

Current processes to control the virus are time-consuming as they require growing the pathogen in the lab, deactivating it and injecting the virus that caused a disease.

The USC model has been found to be especially useful during this stage of the pandemic as the coronavirus begins to mutate in populations around the world. Recent variants of the virus that have emerged in the United Kingdom, South Africa and Brazil seem to spread more easily, which scientists say will rapidly lead to many more cases, deaths and hospitalizations.

According to Bogdan, if SARS-CoV-2 becomes uncontrollable by current vaccines, or if new vaccines are needed to deal with other emerging viruses, then USC’s AI-assisted method can be used to design other preventive mechanisms quickly.

“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.

 

 

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