HLA Inception (IMAGE)
Caption
Analyzing nearly 6,000 MHC-1 complexes, the research team discovered patterns that can identify these preferences and predict immune responses across a broad range of human populations.
The HLA Inception tool, powered by AI and machine learning, uses the varied charges on the surface of the proteins, also known as the electrostatic signatures, to classify them into 11 different types.
This information can then be used to predict whether the protein fragments, or peptides, MHC-1 are monitoring are self or foreign invaders (non-self).
The researchers also found that patients with a more diverse range of MHC-1 proteins, covering more of the 11 classes, had a higher chance of surviving certain cancer therapies.
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Arizona State University
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