Leveraging AlphaFold 3 for Structural Modeling of Neurological Disorder-Associated Proteins: A Pathway to Precision Medicine

Accurate structural modeling of neurological disorder-causing proteins provides an important layer in unraveling the mechanism of disease and identifying therapeutic targets. This study utilizes AlphaFold 3, a state-of-the-art protein structure prediction platform, to model and interpret cis- and trans-pQTL-derived proteins associated with Alzheimers disease, Parkinsons disease, and stroke. Using the NG00102 dataset, we created a high-resolution structure for more than 1,200 proteins expressed in Brain, CSF, and Plasma, providing tissue-specific protein structure analysis with associated functional implications. AlphaFold 3 predictions have illuminated key structure parameters including sequence length, average pLDDT confidence scores, and overall distribution of residues with confidence of >75% pLDDT. We used these features to determine the set of druggable proteins having optimal sequence lengths of 100-3000 residues, high structural reliability as evidenced by an average pLDDT > 80, and contain large regions of high-confidence residues. Tissue-specific mapping revealed unique mechanisms characterized by both cis and trans-pQTL effects, that have critical functional implications for how these genetic variants act in neurological disease pathways. Protein clusters by structural properties then led to more defined subgroups with potential implications for drug intervention. This integrated effort captures the strength of AlphaFold 3 in linking genetic variation to protein structure and function, providing a scalable pipeline for prioritizing therapeutic targets. Coupling our results with advanced predictive modeling and tissue-specific data sets provides a robust framework for uncovering new mechanisms and druggable targets in the research of Alzheimers, Parkinsons, and stroke. This advances the field toward precision medicine.