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A new technique developed by scientists uses the combination with artificial intelligence, and the method could speed up the ability for new antibody-based drug discovery.
FREMONT, CA: In health care, Artificial Intelligence (AI) has remarkably different applications. That perhaps explains why that technology is developing at fast rates. In the clinical setting, AI works with known parameters, usually running through a classification process based on what works and what doesn't for patients. The potential of AI here is significant, and the early successes are truly interesting.
Antibody therapies can be significantly effective in treating disease, but hurdles can arise when promising candidate antibodies are generated at a large scale. Stresses encountered during manufacturing can disrupt these fragile proteins' structure, resulting in aggrega
tion and loss of activity. This, in turn, limits them from being made into a therapeutic. Antibody therapeutics have revolutionized medicine, and they can be designed to bind to almost any target and are highly specific.
The scientists clone the target proteins into the core of an enzyme, which then breaks down antibiotics in the bacterium. This allows the scientists to directly link the bacteria's antibiotic resistance to how aggregation-prone the antibody fragment is. If the antibody proteins are susceptible to stress, they will unfold together, become inactive, and the antibiotic will kill the bacteria. However, if the protein chain is more stable, the bacteria will display antimicrobial resistance and evolve in the antibiotic's presence. The scientists harvest the bacteria that have survived and identify the cloned protein sequence, indicating which protein sequences to take forward in the development pipeline.
The scientists make nature do the function by leveraging the idea of natural selection, where mutations or changes take place in the proteins, making them even more stable. The way involves applying evolutionary pressure through natural selection with antibiotics, choosing for the survival of bacteria that generate the protein variants that do not aggregate. The resisting proteins are then sequenced and scored to choose the best ones, followed by a test to see if they retain the original target's binding potential.
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