Leveraging AlphaFold 3 for Structural Modeling of Neurological Disorder-Associated Proteins: A Pathway to Precision Medicine
Bibliographic record
Abstract
Abstract 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 Alzheimer’s disease, Parkinson’s 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 Alzheimer’s, Parkinson’s, and stroke. This advances the field toward precision medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".