This technology is a computational platform that integrates Mendelian randomization with AlphaFold3 to identify the causal protein biomarkers in Alzheimer’s Disease and guide drug target discovery.
Current methods for identifying causal biomarkers in Alzheimer’s disease are limited by their inability to connect genetic risk and functional protein changes. While Mendelian randomization offers ways to infer causality, it often captures unrelated signals and lacks insight into how mutations impact structure or function. This gap makes it incredibly challenging to prioritize biologically relevant targets in drug development. Addressing these shortcomings is essential to advancing more precise diagnostics and effective therapies for Alzheimer’s and other neurodegenerative diseases.
This technology combines a genetic analysis method with AlphaFold3-based protein structure prediction to identify proteins that are causally linked to Alzheimer’s disease. By analyzing large-scale genetic datasets, it identifies mutations associated with disease risk and models how these mutations may alter protein structure. This approach helps bridge the gap between genetic associations and functional protein changes, which will enable researchers to better prioritize therapeutic targets.
Initial analyses using Alzheimer’s Disease datasets identified seven target proteins with potential roles in the disease, along with structural models showing how specific mutations may impact their function.
IR CU25371
Licensing Contact: Joan Martinez
