2024/11/15 by Aaron Logsdon, Roghaiyeh Safari, Anna Barnard +2 · 2 voices
Biochemistry, Genetics and Molecular Biology · #Biomedical Text Mining and Ontologies #Genetics, Bioinformatics, and Biomedical Research #Bioinformatics and Genomic Networks
paper · pdf · doi:10.1101/2024.11.12.623182
openalex publication_date 2024/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Abstract Background Accurate interpretation of missense variants remains a significant challenge hindering genomic diagnosis. While state-of-the-art machine learning and deep learning predictors offer high accuracy, they often lack the transparency required for clinical evidence weighting. To bridge this gap between accuracy and clinical utility, we developed AlphaRING-X. Methods AlphaRING-X is an open-source framework that integrates local and global protein structural stability features using an efficient implementation of gradient boosting, XGBoost, to predict variant deleteriousness. Local stability is interrogated through a combination of AlphaFold-based modelling and residue interaction network analysis. Changes in global stability upon mutation (ΔΔG) are estimated using FoldX. For each prediction, AlphaRING-X quantifies the magnitude and direction of each feature’s contribution using Shapley additive explanations (SHAP), providing a unified yet interpretable score. We trained and evaluated the model using a gold standard ClinVar dataset of validated neutral and deleterious variants. Results AlphaRING-X achieved an area under the receiver operator curve of 0.94, significantly outperforming widely used predictors such as CADD. Crucially, it provided unambiguous classification for 95.5% of variants at 90% precision in both neutral and deleterious classes. We analysed each prediction’s SHAP values, identifying local connectivity and disorder as the strongest contributors. Conclusions AlphaRING-X combines high-performance prediction with the interpretability necessary for clinical decision-making. Its flexible, open-source architecture makes it a powerful, transparent tool for enhancing genomic diagnostics and advancing precision medicine.