AI Designed Conformation Locking Peptides Target STING to Restore Diabetic Wound Healing

Area of Science:

• Biomaterials Science
• Immunology
• Computational Biology

Background:

• Diabetic foot ulcers involve persistent inflammation and impaired healing, partly due to the cGAS-STING innate immune pathway.
• Targeting this pathway in the protease-rich wound microenvironment is difficult due to challenges in localized retention and responsiveness.

Purpose of the Study:

• To develop an AI-to-biomaterial strategy for precise immunomodulation and enhanced repair of diabetic wounds.
• To identify and deliver a novel peptide that inhibits the cGAS-STING pathway in a microenvironment-responsive manner.

Main Methods:

• Utilized a deep-learning pipeline (RFDiffusion, ProteinMPNN, AlphaFold2-multimer) to discover SCP-1, a peptide stabilizing the inactive STING dimer.
• Incorporated SCP-1 into a dual-responsive hydrogel (Gel-SCP-1) for in situ gelation and MMP-9-triggered release.
• Evaluated Gel-SCP-1's efficacy in a diabetic mouse wound model.

Main Results:

• Gel-SCP-1 suppressed STING-TBK1-IRF3 signaling, reducing inflammation and oxidative stress.
• The hydrogel promoted reparative macrophage polarization and enhanced angiogenic activity.
• Gel-SCP-1 significantly accelerated wound closure and improved tissue regeneration, including re-epithelialization and collagen remodeling in diabetic mice.

Conclusions:

• An integrated AI-to-biomaterial strategy offers a novel therapeutic paradigm for chronic diabetic wounds.
• This approach enables precision immunoregenerative therapy by targeting specific immune pathways.
• The developed hydrogel system demonstrates potential for effective localized and responsive drug delivery in wound healing.