PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects
Predicting the functional impact of point mutations is a critical challenge in genomics. PRESCOTT reconstructs complete mutational landscapes, identifies mutation-sensitive regions, and categorizes missense variants as benign, pathogenic, or variants of uncertain significance. Leveraging protein sequences, structural models, and population-specific allele frequencies, PRESCOTT surpasses existing methods in classifying ClinVar variants, the ACMG dataset, and over 1800 proteins from the Human Protein Dataset. Its online server facilitates mutation effect predictions for any protein and variant, and includes a database of over 19,000 human proteins, ready for population-specific analyses. Open access to residue-specific scores offers transparency and valuable insights for genomic medicine.
Authors
- Mustafa Tekpinar (ORCID: https://orcid.org/0000-0002-0207-0446)
- Alessandra Carbone (ORCID: https://orcid.org/0000-0003-2098-5743)
- Thomas Henry (ORCID: https://orcid.org/0000-0002-0687-8565)
- Laurent David (ORCID: https://orcid.org/0000-0003-3594-0353)
Institutions
- Université Claude Bernard Lyon 1 (FR)
- École Normale Supérieure de Lyon (FR)
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Institut Universitaire de France (FR)
- Sorbonne Université (FR)
- Biologie Computationnelle, Quantitative et Synthétique (FR)
- Centre International de Recherche en Infectiologie (FR)
Publication Details
- Journal
- Genome biology
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1186/s13059-025-03581-y
- Citations
- 5
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 9.09
Funders
- Agence Nationale de la Recherche
- Grand Équipement National De Calcul Intensif