hape that helps it evade antibodies. Therefore, training an NLP with genetic sequences allows the model to predict new sequences, which still follow the rules biological rules of protein structure but have a different appearance.
The researchers said, some of the benefits of using NLP models for this application included that they can be trained using only genetic sequence information, which is much easier to obtain than protein structures, and that this training requires a relatively small amount of information – in their study, the researchers used 60,000 HIV sequences, 45,000 influenza sequences and 4,000 coronavirus sequences.
Predicting promising vaccine targets
Once the model was trained, the researchers used it to predict sequences of the coronavirus Spike (S) protein, HIV envelope protein and influenza hemagglutinin (HA) protein that would be more or less likely to generate escape mutations.
The model suggested:
- The sequences least likely to mutate in influenza were in the stalk of the HA protein. Unfortunately, most people infected with the flu or vaccinated against it do not develop antibodies against the HA stalk.
- For coronaviruses, a part of the S protein called the S2 subunit is least likely to generate escape mutations.
- In their studies of HIV, the researchers found that the V1-V2 hypervariable region of the envelope protein has many possible escape mutations, as well as identifying some sequences that would have a lower probability of escape.
The researchers are now working with others to use their model to identify possible targets for cancer vaccines that stimulate the immune system to destroy tumours. They said it could also be used to design small-molecule drugs that might be less likely to provoke treatment resistance, for diseases such as tuberculosis.
“There are so many opportunities and the beautiful thing is all we need is sequence data, which is easy to produce,” concluded Bryan Bryson, one of the senior authors of the paper published in Science, an assistant professor of biological engineering at MIT and a member of the Ragon Institute of MGH, MIT and Harvard.