Predicting the pathogenicity of missense variants using features derived from AlphaFold2
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Abstract
Motivation: Missense 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. Results: To 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.
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Calibrated Variant Effect Prediction at the Residue Level Using Conditional Score DistributionsPreprint
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- Authors
- Schmidt, A., Röner, S., Mai, K., Klinkhammer, H., Kircher, M., & Ludwig, K. U.
- Affiliations
- University of Bonn (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Corresponding author
- Schmidt, Axel
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-04-21
- Volume
- 39
- Issue
- 5
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC10203375
- OpenAlex
- W4366605966
- Cited by
- 79
- References
- 49
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 24 · 2023: 12 · 2022: 4
- FWCI
- 15.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- University of Bonn
- German Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- missense mutations, pathogenicity prediction, AlphaFold2, protein structure, random forest, solvent accessibility, deep mutational scanning, ClinVar, monogenic disorders, gnomAD, CADD, REVEL, feature engineering
- MeSH terms
- Artificial Intelligence, Humans, Mutation, Virulence, Computational Biology, Mutation, Missense
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