A new artificial intelligence model could help design antibodies which better protect the body against viruses and disease, finds a new study by UCL and University of Surrey researchers.
The AI model, known as ImmunoMatch, can predict and identify correct protein pairings within antibodies, potentially helping to strengthen the immune system.
For this unique study, published in Nature Methods, scientists sought to understand if artificial intelligence could be used to predict how the interior of antibodies are assembled in the body. Antibodies, which are comprised of 'heavy' and 'light' protein chains, are produced by B cells within the immune system and protect against viruses and bacteria.
Co-lead author Professor Franca Fraternali, based at the Institute of Structural and Molecular Biology, a joint institute between UCL and Birkbeck, said: "Until now, it was widely assumed that the pairing of heavy and light chains within antibodies occurred at random. Using Immunomatch, we show for the first time that this assembly is, in fact, highly specific.
"Understanding these pairing rules is crucial for predicting antibody stability and performance and opens the door to the rational design of more effective therapeutics."
To learn more, scientists created ImmunoMatch, based on an antibody-specific language model, which was applied to heavy and light chain antibody sequences collected from millions of single human B cells. The AI model was able to identify and predict pairings of chains, giving scientists an invaluable insight into how antibodies are combined.
The team also showed that ImmunoMatch can accurately analyse antibody sequences from immune cells actively responding to disease, including those from haematological cancers and B cells within solid tumours. These insights could accelerate the rational design of new therapeutic antibodies.
Co-author Professor Deborah Dunn-Walters (University of Surrey) said: "Using AI has helped us discover that the combinations of 'heavy' and 'light' chains are not as random as we previously thought.
"This information enables us to learn the nature-derived rules governing how proteins are combined to create functional antibodies.
"Antibodies are the single largest class of modern therapeutics. Around a quarter of all newly approved therapeutics are monoclonal antibodies, so understanding how antibodies are made is critical for their effective design."
