Robotics and AI used to stabilize enzyme that shrinks, grows scar tissue

Researchers at Rutgers University, working on a grant from the US National Science Foundation, have immobilized an enzyme that can reverse and regenerate tissue damage from spinal cord injuries.

Using artificial intelligence and robotics, the team designed therapeutic proteins that help repair damaged spinal cord tissue. Scientists published their research in advanced health care supplies,

Enzymes immobilized using AI and robotics can degrade scar tissue and promote regeneration. Photo courtesy Dr.VH Perez-Perez via Wikimedia, CC-BY-SA-4.0

ChABC, the enzyme the team immobilized, is unstable and has a short shelf life under clinical conditions. The compound can repair scar tissue molecules and promote regeneration, but the logistics and expense of multiple high-cost infusions have limited its efficacy. Stabilizing ChABC is the key to developing economical and functional therapeutic applications.

“This study represents the first time artificial intelligence and robotics has been used to create highly sensitive therapeutic proteins and increase their activity to such a large extent,” said principal investigator Adam Gormley. “The therapy may someday help people reduce scarring on their spine and regain function.

Following a spinal cord injury, secondary inflammation produces dense scar tissue that may inhibit or inhibit tissue regeneration. Treatments developed as a result of this research can reduce the primary and secondary effects of spinal cord trauma, resulting in treatments that are more accessible, affordable and sustainable.

“This inspiring result demonstrates the excellent implementation of the Materials Genome Initiative and NSF’s research philosophy of Designing Materials to revolutionize and engineer our future program,” said John Schlueter, a program director in NSF’s Department of Materials Research. “By integrating data-driven optimization, robotic polymer synthesis, and high throughput testing, these researchers have made significant improvements in intact enzyme activity after three iterations of active learning.”

Source: NSF


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