A computer-designed antigen triggered immune responses in mice against three coronaviruses responsible for major human outbreaks.
The researchers developed a computational method called Spectravax. It selects parts of a virus that could be recognized by T cells, cells capable of identifying infected cells. The study, published in Cell Reports Methods, focuses on SARS-CoV, SARS-CoV-2, and MERS-CoV.
Arini izzati · CC BY-SA 4.0 · Wikimedia
To understand the approach, vaccines must present the immune system with recognizable elements of the virus. However, coronaviruses evolve and their proteins differ. The researchers therefore sought fragments sufficiently conserved across several viruses, while also taking into account genetic differences between people that influence immune recognition.
Spectravax combines these two dimensions. The program analyzes the diversity of viral sequences and estimates which fragments can be presented effectively to the human immune system. The goal is to construct a single antigen likely to be recognized across a broad population and against several related coronaviruses.
The team focused on the nucleocapsid, a protein located inside the virus and associated with its genetic material. It is less exposed than the well-known Spike protein of SARS-CoV-2. However, some of its parts remain similar across different coronaviruses, making it a possible target for a shared immune response.
After the computational phase, the researchers tested the resulting antigen in mice. The animals developed T-cell responses reacting to SARS-CoV, SARS-CoV-2, and MERS-CoV. The researchers also compared their construct with natural viral sequences and other computer-designed models, which produced more limited responses in their experiments.
The analyses then made it possible to identify specific fragments from the MERS-CoV nucleocapsid that contribute to this cross-reaction. In other words, immune cells trained with the artificial antigen can recognize elements present in several different members of this viral family.
The method now provides a testable strategy for preparing vaccines covering several coronaviruses rather than just one. The next steps will include verifying protection against infection in suitable models, then determining whether the responses predicted by computation are observed amid human immune diversity.