AI-Assisted Computer Model Predicts Potential Coronavirus Vaccines and Could Help Counter COVID-19 Mutations

The method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identified - solutions that give humans a big advantage over the evolving contagion. Their machine-learning model can accomplish vaccine design cycles that once took months or years in a matter of seconds and minutes, the study says.

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, the study says. 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. Moreover, 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 newly-developed AI-assisted method will be especially useful during this stage of the pandemic as the coronavirus begins to mutate in populations around the world. Some scientists are concerned that the mutations may minimize the effectiveness of vaccines by Pfizer and Moderna, which are now being distributed. Recent variants of the virus that have emerged in the UK, South Africa and Brazil seem to spread more easily, which scientists say will rapidly lead to many more cases, deaths and hospitalizations. However, the scientists believe that if SARS-CoV-2 becomes uncontrollable by current vaccines, or if new vaccines are needed to deal with other emerging viruses, then the method can be used to design other preventive mechanisms quickly. For example, the study explains that the USC scientists used only one B-cell epitope and one T-cell epitope, whereas applying a bigger dataset and more possible combinations can develop a more comprehensive and quicker vaccine design tool. The study estimates the method can perform accurate predictions with over 700,000 different proteins in the dataset.

“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.”

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