Wilmer Eye Institute, Johns Hopkins Medicine researchers say they have used artificial intelligence models and machine-learning algorithms to successfully predict which components of amino acids that make up therapeutic proteins are most likely to safely deliver therapeutic drugs to animal eye cells.
The new research showed that artificial intelligence-designed models accurately predicted an effective sequence of amino acids, also known as peptides or small proteins, that would bind to a particular chemical in rabbit eye cells and safely dispense medications over several weeks, reducing the need for frequent, strict treatment schedules. The team specifically investigated peptides that bind to melanin, a compound that provides color to the eye but has the advantage of being widely present throughout specialized structures in eye cells.
The artificial intelligence model generated 127 peptides that were predicted to have varying ability to penetrate the specialized cells that house melanin, to bind to melanin and to be nontoxic to the cells. Out of these 127 peptides, the model predicted that a peptide called HR97 had the highest success rate of binding. The team also confirmed the properties of these peptides, including better uptake and binding within cells as well as no indication of cell death.