Structure-Based Network Analysis of AlphaFold Structure Predictions Identifies Putative Causative Variants of...

As sequencing improves, identifying variants causing inherited retinal diseases (IRDs) is essential for gene therapy. Structure-based network analysis (SBNA) predicts missense variant impact based entirely on structural first principles rather than historical phenotypic or clinical outcome data, distinguishing it among contemporary missense prediction tools. Here, we expanded the application of SBNA to artificial intelligence (AI)-generated protein structures, facilitating application to all known IRD-associated proteins.

We first calculated SBNA scores for structures from the Protein Data Bank (PDB) and AI-generated structures from AlphaFold2, comparing scores for pathogenic and benign ClinVar variants. We then used these results to identify the putative genetic basis of disease for patients with IRDs, demonstrating the clinical applicability of this approach.

We found a significant difference between SBNA scores for known benign and pathogenic variants across all human protein structures from the PDB (median, -0.6 vs. 1.8; P < 0.0001; AUC = 0.763) and across the corresponding AlphaFold2 structures (median, -0.2 vs. 1.9; P < 0.0001; AUC = 0.755). This difference was also significant for AlphaFold2 structures from 374 IRD-associated proteins (median, -0.4 vs. 1.9; P < 0.0001; AUC = 0.779), including 185 without available structural data. This model identified likely causative disease variants in 56% of IRD patients without a known genetic basis for disease.

SBNA can identify variants in human proteins that are likely to cause disease, and it can help predict variants causative of IRDs in an unbiased fashion using both AlphaFold2-generated structural models and experimental structural data.