DOI: 10.5281/zenodo.18213611
title: Curated AlphaFold-Derived Structural Feature Dataset for Rare Disease Variant Classification

description: Overview

This repository contains a curated, feature-engineered dataset developed to support research into the use of AlphaFold protein structure predictions for early-stage rare disease variant interpretation. The dataset was constructed by integrating multiple open-access biological resources and transforming them into a unified, machine-learning-ready format.

The dataset is intended to support reproducible research into structure-aware pathogenicity prediction and AI-assisted diagnostic decision support within HealthTech contexts.

Data Sources

All raw data used in constructing this dataset were obtained from publicly available, open-access biological databases:





ClinVarSource of clinically interpreted genetic variants and pathogenicity labels.Variants classified as Pathogenic, Likely pathogenic, Benign, or Likely benign were used.




AlphaFold Protein Structure Database (AlphaFold DB)Source of predicted three-dimensional protein structures and per-residue confidence scores (pLDDT).



Original data ownership remains with the respective providers. This dataset represents a derived and transformed research artefact, not a redistribution of raw source data.

 

Feature Engineering and Dataset Construction

The final dataset consists of engineered numerical features designed to capture structural confidence and predicted perturbation effects associated with genetic variants.

Key feature engineering steps included:





Protein-level structural confidence extraction





Average pLDDT score




pLDDT standard deviation




Counts of high-confidence and low-confidence residues




Protein-level confidence ratios






Variant-level structural impact estimation





Heuristic estimation of variant-specific pLDDT values




Penalisation of terminal residues to reflect known AlphaFold uncertainty




Differential perturbation modelling for pathogenic versus benign variants






Derived comparative features





Difference between variant-level and protein-average confidence




Normalised confidence deviation metrics





All features were generated programmatically using Python and standard scientific computing libraries. The dataset was cleaned, filtered, and validated to ensure consistency and suitability for supervised machine learning.

 

Licensing and Data Ethics

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence.





No patient-identifiable or sensitive personal data are included.




All source databases are publicly accessible and ethically approved for research use.




Users are required to cite the original data providers when reusing or extending this dataset.



The dataset complies with research data governance principles, including transparency, reproducibility, and responsible reuse.

 

Intended Use

This dataset is provided strictly for research and educational purposes.

It is intended to support:





Methodological research in AI-assisted genomics




Evaluation of AlphaFold-derived structural features in variant classification




Development of explainable machine learning models for rare disease research



The dataset must not be used for direct clinical diagnosis or decision-making without appropriate clinical validation and regulatory approval.