ArticleBioinformatics (Oxford, England)2023
Predicting the pathogenicity of missense variants using features derived from AlphaFold2.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 64 citations in OpenAlex.
- Probabilistic mapping of sub-genic intolerance reveals functional and disease-critical protein regions.bioRxiv : the preprint server for biology · 2026Article
- Prediction of human missense variant effects from functional evidence.Nature genetics · 2026Article
- Article
- Pathogenicity Prediction of Missense Variations in Hereditary Cancer Genes.International journal of molecular sciences · 2026Article
- EMMVEP: An Ensemble Method for Protein Missense Variant Effect Prediction Based on Multi-Source Feature Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- Deep DNA and protein level feature integration for robust clinical variant interpretation using probabilistic gradient boosting.Frontiers in digital health · 2026Article
- Reclassification of missense variant pathogenicity using ClinGen recommendations for recalibrated PP3/BP4 in silico predictor score thresholds.Genetics in medicine open · 2026Article
- Classification of driver and passenger mutations in different cancer types using deep neural networks.Bioinformatics advances · 2026Article
- Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score Distributions.bioRxiv : the preprint server for biology · 2025Article
- Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.NAR genomics and bioinformatics · 2025Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- MOLGENIS VIP: an end-to-end DNA variant interpretation pipeline for research and diagnostics configurable to support rapid implementation of new methods.NAR genomics and bioinformatics · 2025Article
- Detection of protein structural hotspots using AI distillation and explainability: application to the DAX-1 protein.NAR genomics and bioinformatics · 2025Article
- PRP: pathogenic risk prediction for rare nonsynonymous single nucleotide variants.Human genetics · 2025Article
- Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.Journal of integrative bioinformatics · 2025Article
- Variant effect predictor correlation with functional assays is reflective of clinical classification performance.Genome biology · 2025Article
- Deep learning tools predict variants in disordered regions with lower sensitivity.BMC genomics · 2025Article
- AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.Protein science : a publication of the Protein Society · 2025Article
- Deep learning-based assessment of missense variants in theBMJ open ophthalmology · 2025Article
- Missense variant analysis in the TRPV1 ARD reveals the unexpected functional significance of a methionine.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationMissense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major challenge in the context of personalized medicine. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2. This raises the question of whether AlphaFold2 wild-type structures can improve the accuracy of computational pathogenicity prediction for missense variants.
resultsTo address this, we first engineered a set of features for each amino acid from these structures. We then trained a random forest to distinguish between relatively common (proxy-benign) and singleton (proxy-pathogenic) missense variants from gnomAD v3.1. This yielded a novel AlphaFold2-based pathogenicity prediction score, termed AlphScore. Important feature classes used by AlphScore are solvent accessibility, amino acid network related features, features describing the physicochemical environment, and AlphaFold2's quality parameter (predicted local distance difference test). AlphScore alone showed lower performance than existing in silico scores used for missense prediction, such as CADD or REVEL. However, when AlphScore was added to those scores, the performance increased, as measured by the approximation of deep mutational scan data, as well as the prediction of expert-curated missense variants from the ClinVar database. Overall, our data indicate that the integration of AlphaFold2-predicted structures can improve pathogenicity prediction of missense variants. AVAILABILITY AND IMPLEMENTATION: AlphScore, combinations of AlphScore with existing scores, as well as variants used for training and testing are publicly available.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.