AI Model EVE-Vax Predicts Viral Evolution to Enhance Vaccine Development

Building vaccines for future versions of a virus

Effective vaccines dramatically changed the course of the COVID-19 pandemic, preventing illness, reducing disease severity, and saving millions of lives. However, five years later, SARS-CoV-2 is still circulating, and in the process, evolving into new variants that require updated vaccines to protect against them. But it takes time to design, manufacture, and distribute a new vaccine, which raises an important question: How can scientists create vaccines for versions of the virus that haven't happened yet? One solution comes from a predictive AI model called EVE-Vax built by a team of scientists at Harvard Medical School, the HMS-led Massachusetts Consortium on Pathogen Readiness (MassCPR), and other institutions. The new model, described May 8 in Immunity, uses evolutionary, biological, and structural information about a virus to predict and design surface proteins likely to occur as the pathogen mutates. The researchers successfully applied EVE-Vax to SARS-CoV-2, designing viral proteins that elicited similar immune responses as the actual proteins that evolved during the COVID-19 pandemic. The research, which was supported in part by federal funding, suggests that the model provides valuable information about the future evolution of a virus and can be used to create panels of "designer" proteins to evaluate the future protection of vaccines. The researchers hope that the model will eventually help scientists develop better vaccines to combat viral outbreaks and pandemics caused by rapidly mutating viruses. "We show that if you can see where a virus is evolving ahead of time, you can begin to make future-proof vaccines," said first author Noor Youssef, a scientific lead for the Predictive Modeling for Vaccine Design group in the Marks lab at HMS. The evolution of EVE Over a decade ago, study co-senior author Debora Marks, professor of systems biology in the Blavatnik Institute at HMS, and her lab began exploring whether they could use millions of years of evolutionary genetic information to make predictions about the structure and function of proteins. In a 2021 paper, the researchers described an AI model they created based on this idea. The model, called EVE (evolutionary model of variant effect), uses large-scale evolutionary data across species to predict whether proteins will be functional. When applied to humans, EVE was able to interpret gene variants as benign or disease-causing. As the COVID-19 pandemic unfolded, Marks and her team adapted their model to predict viral behavior. They built EVEscape, which applied EVE's powers of protein prediction to viral proteins. In a 2023paper, the scientists showed that had EVEscape existed at the start of the COVID-19 pandemic, it would have predicted the most frequent SARS-CoV-2 mutations and spotted the variants of greatest concern that were most likely to cause a spike in human infections. The success of EVEscape led the researchers to wonder whether their model could also forecast the future evolution of rapidly evolving viruses such as SARS-CoV-2. Vaccines for such viruses are updated annually, which requires scientists to make an educated guess up to a year in advance about how viral proteins will evolve. This can lead to a mismatch between the predicted version of a virus used to design the vaccine and the actual version that ends up circulating. The gap between prediction and reality can cause vaccines to be less effective than they would have been if the versions matched more closely. To solve this problem, the researchers developed EVE-Vax, a model that predicts and designs viral proteins that can be used to inform vaccine development ahead of time. "We wanted to see if we could use our methods to create brand-new proteins that would be functional and would have the same immune response that we see with real viruses," Youssef said. Predicting SARS-CoV-2's future maneuvers In their latest research, the scientists used EVE-Vax to design 83 brand-new versions of the "spike" protein on SARS-CoV-2, which is the main surface protein the virus uses to infect human cells. Each new version of the spike protein had a different combination of up to ten mutations. To test the effect of the AI-designed proteins, researchers teamed up with experimental colleagues and co-senior authors Jeremy Luban, professor of molecular medicine at UMass Chan Medical School; Jacob Lemieux, HMS assistant professor of medicine at Massachusetts General Hospital; and Michael Seaman, HMS associate professor of medicine at Beth Israel Deaconess Medical Center. The scientists performed experiments in lab dishes using safe, nonreplicating versions of SARS-CoV-2 engineered for the research. The experiments confirmed that viruses harboring the "designer" spike proteins infected human cells and elicited immune responses that largely matched the real-life immune responses to the virus at five different timepoints during the COVID-19 pandemic. "The fundamental insight here is that evolution tells you what's possible for the virus and its proteins to do and what might happen in the future," Marks said. Finally, the researchers showed that they could easily and cheaply engineer hundreds of new spike proteins that could be readily incorporated into vaccine development for SARS-CoV-2. "Traditional vaccine design uses all sorts of different methods, but nobody's used this approach before," Marks said. "EVE-Vax opens a new field of potential application and design for vaccines." For example, the researchers showed that with EVE-Vax, they could have predicted that there would be considerable immune escape from the COVID-19 booster vaccine targeting the Omicron variant -- knowledge that would have clued in scientists to build the booster differently. "With EVE-Vax, we can predict the immune response, instead of just what the mutations on a virus will be, which is more useful in real-world situations," Marks said. The team is now broadening EVE-Vax to other viruses, including avian influenza, a growing problem in the United States and worldwide. One key advantage of the model, the researchers noted, is that it can work with limited information, which opens it up for understudied viruses such as Lassa and Nipah, as well as newly emerging viruses. Ultimately, the team hopes that EVE-Vax will give scientists critical information about the various ways in which a virus is likely to evolve, enabling them to design vaccines that protect against future versions of the pathogen. Authorship, funding, disclosures Additional authors on the paper include Sarah Gurev, Fadi Ghantous, Kelly Brock, Javier Jaimes, Nicole Thadani, Ann Dauphin, Amy Sherman, Leonid Yurkovetskiy, Daria Soto, Ralph Estanboulieh, Ben Kotzen, Pascal Notin, Aaron Kollasch, Alexander Cohen, Sandra Dross, Jesse Erasmus, Deborah Fuller, and Pamela Bjorkman. Funding for the work was provided by the Coalition for Epidemic Preparedness Innovations (CEPI), MassCPR, and the National Institutes of Health (R37 AI147868). Marks is an advisor for Dyno Therapeutics, Octant, Jura Bio, Tectonic Therapeutic, and Genentech and is a cofounder of Seismic Therapeutic.

Building Vaccines for Future Versions of a Virus | Newswise

AI model EVE-Vax provides clues about how a virus may evolve and the immune response it could provoke Newswise -- Effective vaccines dramatically changed the course of the COVID-19 pandemic, preventing illness, reducing disease severity, and saving millions of lives. However, five years later, SARS-CoV-2 is still circulating, and in the process, evolving into new variants that require updated vaccines to protect against them. But it takes time to design, manufacture, and distribute a new vaccine, which raises an important question: How can scientists create vaccines for versions of the virus that haven't happened yet? One solution comes from a predictive AI model called EVE-Vax built by a team of scientists at Harvard Medical School, the HMS-led Massachusetts Consortium on Pathogen Readiness (MassCPR), and other institutions. The new model, described May 8 in Immunity, uses evolutionary, biological, and structural information about a virus to predict and design surface proteins likely to occur as the pathogen mutates. The researchers successfully applied EVE-Vax to SARS-CoV-2, designing viral proteins that elicited similar immune responses as the actual proteins that evolved during the COVID-19 pandemic. The research, which was supported in part by federal funding, suggests that the model provides valuable information about the future evolution of a virus and can be used to create panels of "designer" proteins to evaluate the future protection of vaccines. The researchers hope that the model will eventually help scientists develop better vaccines to combat viral outbreaks and pandemics caused by rapidly mutating viruses. "We show that if you can see where a virus is evolving ahead of time, you can begin to make future-proof vaccines," said first author Noor Youssef, a scientific lead for the Predictive Modeling for Vaccine Design group in the Marks lab at HMS. The evolution of EVE Over a decade ago, study co-senior author Debora Marks, professor of systems biology in the Blavatnik Institute at HMS, and her lab began exploring whether they could use millions of years of evolutionary genetic information to make predictions about the structure and function of proteins. In a 2021 paper, the researchers described an AI model they created based on this idea. The model, called EVE (evolutionary model of variant effect), uses large-scale evolutionary data across species to predict whether proteins will be functional. When applied to humans, EVE was able to interpret gene variants as benign or disease-causing. As the COVID-19 pandemic unfolded, Marks and her team adapted their model to predict viral behavior. They built EVEscape, which applied EVE's powers of protein prediction to viral proteins. In a 2023 paper, the scientists showed that had EVEscape existed at the start of the COVID-19 pandemic, it would have predicted the most frequent SARS-CoV-2 mutations and spotted the variants of greatest concern that were most likely to cause a spike in human infections. The success of EVEscape led the researchers to wonder whether their model could also forecast the future evolution of rapidly evolving viruses such as SARS-CoV-2. Vaccines for such viruses are updated annually, which requires scientists to make an educated guess up to a year in advance about how viral proteins will evolve. This can lead to a mismatch between the predicted version of a virus used to design the vaccine and the actual version that ends up circulating. The gap between prediction and reality can cause vaccines to be less effective than they would have been if the versions matched more closely. To solve this problem, the researchers developed EVE-Vax, a model that predicts and designs viral proteins that can be used to inform vaccine development ahead of time. "We wanted to see if we could use our methods to create brand-new proteins that would be functional and would have the same immune response that we see with real viruses," Youssef said. Predicting SARS-CoV-2's future maneuvers In their latest research, the scientists used EVE-Vax to design 83 brand-new versions of the "spike" protein on SARS-CoV-2, which is the main surface protein the virus uses to infect human cells. Each new version of the spike protein had a different combination of up to ten mutations. To test the effect of the AI-designed proteins, researchers teamed up with experimental colleagues and co-senior authors Jeremy Luban, professor of molecular medicine at UMass Chan Medical School; Jacob Lemieux, HMS assistant professor of medicine at Massachusetts General Hospital; and Michael Seaman, HMS associate professor of medicine at Beth Israel Deaconess Medical Center. The scientists performed experiments in lab dishes using safe, nonreplicating versions of SARS-CoV-2 engineered for the research. The experiments confirmed that viruses harboring the "designer" spike proteins infected human cells and elicited immune responses that largely matched the real-life immune responses to the virus at five different timepoints during the COVID-19 pandemic. "The fundamental insight here is that evolution tells you what's possible for the virus and its proteins to do and what might happen in the future," Marks said. Finally, the researchers showed that they could easily and cheaply engineer hundreds of new spike proteins that could be readily incorporated into vaccine development for SARS-CoV-2. "Traditional vaccine design uses all sorts of different methods, but nobody's used this approach before," Marks said. "EVE-Vax opens a new field of potential application and design for vaccines." For example, the researchers showed that with EVE-Vax, they could have predicted that there would be considerable immune escape from the COVID-19 booster vaccine targeting the Omicron variant -- knowledge that would have clued in scientists to build the booster differently. "With EVE-Vax, we can predict the immune response, instead of just what the mutations on a virus will be, which is more useful in real-world situations," Marks said. The team is now broadening EVE-Vax to other viruses, including avian influenza, a growing problem in the United States and worldwide. One key advantage of the model, the researchers noted, is that it can work with limited information, which opens it up for understudied viruses such as Lassa and Nipah, as well as newly emerging viruses. Ultimately, the team hopes that EVE-Vax will give scientists critical information about the various ways in which a virus is likely to evolve, enabling them to design vaccines that protect against future versions of the pathogen. Authorship, funding, disclosures Additional authors on the paper include Sarah Gurev, Fadi Ghantous, Kelly Brock, Javier Jaimes, Nicole Thadani, Ann Dauphin, Amy Sherman, Leonid Yurkovetskiy, Daria Soto, Ralph Estanboulieh, Ben Kotzen, Pascal Notin, Aaron Kollasch, Alexander Cohen, Sandra Dross, Jesse Erasmus, Deborah Fuller, and Pamela Bjorkman. Funding for the work was provided by the Coalition for Epidemic Preparedness Innovations (CEPI), MassCPR, and the National Institutes of Health (R37 AI147868). Marks is an advisor for Dyno Therapeutics, Octant, Jura Bio, Tectonic Therapeutic, and Genentech and is a cofounder of Seismic Therapeutic.