PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects

Tekpinar M., David L., Henry T., Carbone A.

Genome Biology, vol.26, no.1, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 1
  • Publication Date: 2025
  • Doi Number: 10.1186/s13059-025-03581-y
  • Journal Name: Genome Biology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Directory of Open Access Journals
  • Van Yüzüncü Yıl University Affiliated: Yes

Abstract

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.