AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases

Neurodegenerative diseases encompass a diverse group of disorders ranging from adult-onset conditions such as Alzheimer’s and Parkinson’s disease to pediatric forms including neuronal ceroid lipofuscinoses (NCLs), Niemann-Pick type C (NPC), and infantile neuroaxonal dystrophy (INAD), all of which are characterized by protein misfolding and chronic neuroinflammation. During their occurrence and development, the innate immune system, especially the immune responses mediated by microglia in the central nervous system, plays a crucial regulatory role.

Increasing evidence indicates that misfolded and abnormally aggregated proteins, such as β-amyloid (Aβ), Tau, α-synuclein, and TDP-43, are not only neurotoxic factors but can also act as damage-associated molecular patterns (DAMPs) recognized by innate immune receptors, thereby triggering persistent neuroinflammatory responses. However, traditional experimental and computational methods still have significant limitations in systematically analyzing the “protein misfolding–innate immune activation” mechanism.

In recent years, artificial intelligence has made breakthrough progress in protein structure prediction, multi-conformation modeling, and integration of multi-omics data, providing a new research paradigm for revealing the intrinsic relationship between protein misfolding and innate immunity across the spectrum of neurodegenerative diseases.