The global outbreak of monkeypox virus (MPXV), commonly referred to as mpox, in 2022 underscored the urgent need for more effective, scalable, and targeted medical countermeasures against emerging viral threats. Mpox, a zoonotic virus closely related to smallpox, can cause severe illness characterized by fever, rash, painful lesions, and, in extreme cases, death. Vulnerable populations such as children, pregnant women, and individuals with weakened immune systems face the highest risks. While existing smallpox vaccines provided partial protection during the outbreak, their reliance on weakened whole viruses made them expensive, difficult to manufacture, and unsuitable for widespread rapid deployment. Against this backdrop, a groundbreaking study published in Science Translational Medicine demonstrates how artificial intelligence (AI) can revolutionize vaccine and therapeutic development by identifying precise viral targets that elicit powerful immune responses.
During the 2022 mpox outbreak, the virus spread to numerous countries and infected more than 150,000 people worldwide, resulting in nearly 500 deaths. Public health authorities relied primarily on existing smallpox vaccines to control the spread and protect high-risk individuals. Although effective to a degree, these vaccines are based on live or attenuated viruses, making production complex and limiting scalability. Moreover, whole-virus vaccines often stimulate broad immune responses, some of which may be unnecessary or associated with adverse effects. This highlighted the need for a new generation of vaccines and therapies that are safer, more precise, and easier to manufacture.
An international team of scientists, led by researchers from The University of Texas at Austin and the Fondazione Biotecnopolo di Siena in Italy, addressed this challenge using a novel AI-driven approach. Rather than beginning with the virus itself, the researchers started with the human immune response—a strategy known as “reverse vaccinology.” By studying individuals who had either recovered from mpox infection or had been previously vaccinated, the team isolated antibodies that were naturally effective at neutralizing the virus. This approach allowed the researchers to focus on immune mechanisms already proven to work in humans.
The Italian research team, led by Rino Rappuoli and Emanuele Andreano, identified 12 antibodies from patient blood samples that demonstrated strong neutralizing activity against MPXV. These antibodies were capable of interfering with the virus’s ability to infect cells, indicating that they targeted critical components of the viral surface. However, the precise viral proteins—or antigens—that these antibodies recognized were unknown. This posed a significant obstacle, as identifying the correct antigen is essential for designing targeted vaccines or antibody-based therapies.
MPXV displays approximately 35 different proteins on its surface, many of which could theoretically serve as antibody targets. Experimentally testing each possible antibody–protein pairing would be an extremely time-consuming and resource-intensive process. To overcome this challenge, Jason McLellan and his colleagues at UT Austin turned to AlphaFold 3, an advanced AI model capable of predicting protein structures and interactions with remarkable accuracy. By analyzing how patient-derived antibodies might bind to different viral surface proteins, the AI rapidly narrowed down the most likely candidates.
The results were striking. AlphaFold 3 identified a previously overlooked viral surface protein known as OPG153 as a strong candidate for antibody binding. Subsequent laboratory experiments confirmed the AI’s prediction, demonstrating that OPG153 was indeed the antigen targeted by several of the neutralizing antibodies. This discovery was particularly significant because OPG153 had never before been recognized as a target for neutralizing antibodies, nor had it been considered a viable candidate for vaccine or therapeutic development.
According to McLellan, the use of AI dramatically accelerated the discovery process. What might have taken years using traditional experimental methods was achieved in a fraction of the time. This highlights the transformative role AI can play in biomedical research, particularly in responding rapidly to emerging infectious diseases. By enabling scientists to explore vast biological possibilities efficiently, AI tools can uncover critical insights that might otherwise remain hidden.
The identification of OPG153 as a key antigen opens new possibilities for mpox prevention and treatment. Unlike whole-virus vaccines, a vaccine based on a single viral protein can be easier to produce, more stable, and potentially safer. In animal studies, mice immunized with the OPG153 protein produced strong neutralizing antibodies, suggesting that this antigen could form the basis of an effective subunit vaccine. Such vaccines are generally well-tolerated and can be manufactured at scale using established biotechnology platforms.
Beyond vaccines, the discovery also supports the development of antibody-based therapies. Monoclonal antibodies targeting OPG153 could be administered to infected individuals or those at high risk, providing immediate protection or reducing disease severity. These therapies could be particularly valuable for immunocompromised patients who may not respond adequately to vaccination.
Importantly, the implications of this research extend beyond mpox. MPXV is closely related to the variola virus, which causes smallpox—a disease eradicated globally but still considered a potential biosecurity threat due to its high mortality rate and ease of transmission. Because of this close relationship, antigens like OPG153 may be conserved across poxviruses, raising the possibility of developing improved vaccines or treatments that protect against both mpox and smallpox.
The research team is now focused on refining the OPG153 antigen and optimizing antibody candidates to enhance their effectiveness, stability, and manufacturability. Their long-term goal is to advance these candidates into human clinical trials. To support future development and commercialization, UT Austin has filed a patent application covering the use of OPG153 and its derivatives as vaccine antigens, while the Fondazione Biotecnopolo di Siena has filed a patent for antibodies targeting this protein.
In conclusion, this study represents a major step forward in the fight against mpox and related viral diseases. By combining human immunology, artificial intelligence, and molecular engineering, researchers have demonstrated a powerful new paradigm for vaccine and therapeutic discovery. The success of AI-guided reverse vaccinology in identifying OPG153 underscores the potential of data-driven approaches to accelerate biomedical innovation. As emerging and re-emerging infectious diseases continue to threaten global health, such strategies may prove essential for developing rapid, effective, and equitable medical defenses in the future.
Source: University of Texas at Austin
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