ArticleCell reports methods2024
SIGMA leverages protein structural information to predict the pathogenicity of missense variants.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Prediction of human missense variant effects from functional evidence.Nature genetics · 2026Article
- A functional framework in patient fibroblasts informs ATP7A variant pathogenicity and identifies p.Q990P as a novel cause of distal motor neuropathy.Human molecular genetics · 2026Article
- Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation-Informed Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Pathogenicity prediction for noncanonical splice-altering variants based on multimodal feature fusion.Briefings in bioinformatics · 2026Article
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- EMMVEP: An Ensemble Method for Protein Missense Variant Effect Prediction Based on Multi-Source Feature Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.NAR genomics and bioinformatics · 2025Article
- Classification of GBA1 variants and their impact on Parkinson's disease: an in silico score analysis.NPJ Parkinson's disease · 2025Article
- StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations.Nucleic acids research · 2025Article
- Accurate identification and mechanistic evaluation of pathogenic missense variants withProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
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18 authors.
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Abstract
Leveraging protein structural information to evaluate pathogenicity has been hindered by the scarcity of experimentally determined 3D protein. With the aid of AlphaFold2 predictions, we developed the structure-informed genetic missense mutation assessor (SIGMA) to predict missense variant pathogenicity. In comparison with existing predictors across labeled variant datasets and experimental datasets, SIGMA demonstrates superior performance in predicting missense variant pathogenicity (AUC = 0.933). We found that the relative solvent accessibility of the mutated residue contributed greatly to the predictive ability of SIGMA. We further explored combining SIGMA with other top-tier predictors to create SIGMA+, proving highly effective for variant pathogenicity prediction (AUC = 0.966). To facilitate the application of SIGMA, we pre-computed SIGMA scores for over 48 million possible missense variants across 3,454 disease-associated genes and developed an interactive online platform (https://www.sigma-pred.org/). Overall, by leveraging protein structure information, SIGMA offers an accurate structure-based approach to evaluating the pathogenicity of missense variants.
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