Aug 2026· Investigative Ophthalmology and Visual Science· Vol 67· 0 citations· 53 references
Medicine
TL;DR
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.
Abstract
Purpose 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. Methods 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. Results 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. Conclusions 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.
Proteome-wide prediction and structural modeling of disordered protein interaction interfaces advance characterization of disease-associated variants in disordered protein regions.
D. Hubrich, Jesús Alvarado Valverde, C. Y. Lee et al.· Nature Structural & Molecula...· 0 citations
Abstract Background: ABCA4 variants are the primary cause of Stargardt disease and also contribute to other inherited retinal disorders. Despite this central role, nearly half of all ABCA4 missense variants remain classified as variants of uncertain significance (VUS), limiting genetic diagnosis for many patients. The extracytoplasmic domain 2 (ECD2) harbors a disproportionate share of these unresolved variants yet remains poorly characterized. Methods: We analyzed 244 ECD2 missense variants using a consensus variant effect predictor (VEP) framework integrating PolyPhen-2, SIFT, and MutationTaster. Variants predicted pathogenic by all three tools were designated PAAT (pathogenic across all tools) and prioritized for further evaluation. PAAT variants were assessed using thermodynamic stability modeling ΔΔG and structural analysis, and alignment across ABCA family members was used to identify conserved regions, termed critical conserved motifs (CCMs). Results: PAAT variants were enriched in the central region of ECD2 (Pearson chi-square test, p = 0.0309) and showed a significantly higher frequency of destabilizing ΔΔG values than other variant categories (Cochran-Armitage trend test, p < 0.0001). PAAT variants had 2.2-fold higher odds of occurring within the four identified CCMs (95%: 1.29–3.81), directly linking predicted pathogenicity to sequence conservation. Structural modeling of select variants revealed specific disruptive mechanisms, including steric clashes and altered hydrogen bonding near the retinoid-binding interface. Conclusions: This integrative computational framework identifies four conserved motifs likely critical to ECD2 and ABCA4 function, and yields a prioritized, mechanistically grounded list of ECD2 variants for targeted experimental validation. These findings offer a practical path toward reclassifying ABCA4 VUS and improving genetic diagnosis for patients with Stargardt disease and related inherited retinal disorders.
Jazzlyn S. Jones, Barry Bodt, Subhasis B. Biswas et al.· Research Square· 0 citations
The interpretation of missense variants remains a major challenge in clinical genetics. “Meta-domains” aggregate population and pathogenic variation across homologous Pfam domain instances in the human proteome, providing per-residue context for interpreting variants of uncertain significance (VUS). Our 2019 implementation, MetaDome, is widely used and named in clinical variant-classification guidelines. Here we present the MetaDome 2027 update, featuring a comprehensively updated dataset and GRCh38 support. The redesigned pipeline enables incremental updates of GENCODE, UniProtKB/Swiss-Prot, Pfam, gnomAD, and ClinVar while maintaining 100% sequence-identity gene-to-protein mapping. Annotated Pfam domain instances grew 14.9% from 71,419 to 82,069 and meta-domain-eligible Pfam families (≥2 human occurrences) by 73.3% from 3,334 to 5,778; Pfam domains are annotated to 92% of human proteins. Approximately 43% of mapped protein-coding nucleotides (14.3 million in GRCh38, 13.8 million in GRCh37) are in a meta-domain; in GRCh38 67.9% (37,692 of 55,548) of pathogenic or likely pathogenic ClinVar missense variants fall at such a position. We show how MetaDome helped reclassify a de novo missense VUS in RALA and identify 52,463 ClinVar missense VUS for which meta-domains supply otherwise unavailable pathogenic evidence. MetaDome is freely available at www.metadome.app. Graphical Abstract
Laurens Wiel, Federico Ferraro, Jay Yu et al.· bioRxiv· 0 citations
Abstract Objective To determine whether computational protein‐stability predictions discriminate pathogenic from benign SCN1A missense variants, and to characterize the structural distribution of predicted destabilization among pathogenic variants. Methods On an AlphaFold3‐predicted Nav1.1 structure, FoldX, and Rosetta Cartesian ΔΔG were computed for a single ClinVar snapshot of pathogenic/likely‐pathogenic (P/LP) and benign/likely‐benign (B/LB) missense variants and its extension to ClinVar variants of uncertain significance (VUS) and gnomAD v4.1 variants; membrane‐aware RosettaMP was applied to the patch‐clamp subgroup. Pathogenic variants were stratified by functional domain. Results Pathogenic variants were more destabilizing than benign (FoldX 2.61 vs. 0.31 kcal/mol, p = 1.27 × 10−11; ROC‐AUC = 0.760), concordant with Rosetta (ROC‐AUC = 0.697; ρ = 0.660). Destabilization was domain‐dependent: pore (P‐loop/selectivity‐filter) pathogenic variants were depleted of stability‐neutral variants (0.40‐fold; Bonferroni‐adjusted p = 4.3 × 10−7), whereas S4 voltage‐sensor variants were enriched for them (2.32‐fold; p = 0.013). Across ~3300 non‐redundant variants, gnomAD‐common variants resembled benign controls and VUS were intermediate (mean ΔΔG 0.98 kcal/mol; 18.5% strongly destabilizing), with the domain pattern preserved. Among 64 patch‐clamp variants, stability did not separate gain‐ from loss‐of‐function, though gain‐of‐function variants clustered in voltage‐sensing domains and were absent from the pore. Significance Computational stability analysis thus adds a mechanistic layer complementary to the conventional gating‐dysfunction view, distinguishing a destabilized pore‐region subset—for which proteostasis impairment is a candidate, though unproven, mechanism—from a structurally tolerated S4 subset whose pathogenicity is stability‐independent. As a hypothesis‐generating rather than mechanism‐defining approach, this stratification prioritizes candidate variants—including the 18.5% of VUS that are strongly destabilizing—for direct functional and surface‐expression validation in SCN1A‐related epilepsies. Plain Language Summary We used computational modeling to predict how thousands of SCN1A genetic variants influence the stability of the Nav1.1 sodium channel protein. Disease‐causing variants tended to destabilize the protein more than benign variants, and variants in the pore region—where ions flow through the channel—were predominantly destabilizing. This is consistent with loss‐of‐function arising from misfolding and degradation of the channel protein in this subset of variants. By contrast, variants in the voltage‐sensing region were often structurally tolerated, indicating that their disease‐causing effects likely arise through a different mechanism that requires direct functional measurement to define. Accordingly, the analysis nominates a candidate pore‐region subset potentially affected by proteostasis impairment and a complementary stability‐neutral subset warranting functional evaluation.
Y. Shim, E. Kang, Naeun Kwak et al.· Epilepsia Open· 0 citations
The discordance persists: pLDDT correlates positively with PUNCH2 disorder in random and de novo proteins and negatively with β-strand fraction, opposite to the conserved and disordered baselines, a concrete failure mode that protein designers and other working on sequences remote in sequence space should be aware of when relying on predictor outputs.
Lars A. Eicholt, Lasse Middendorf· bioRxiv· 0 citations
Whether AlphaFold 3 complex prediction, combined with STRING evidence and domain-level analysis of interfaces and interaction partners, can help identify and characterize DUF-containing proteins and suggest roles for DUF4130 in nucleic-acid-associated radical-SAM biology and DUF5819 in a bacterial system related to vitamin-K-dependent carboxylation are suggested.
Lino Riepenhausen, Francesco Costa, Antonina Andreeva et al.· bioRxiv· 0 citations
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