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

Abstract

AbstractPredicting the functional impact of point mutations is a complex yet vital task in genomics. PRESCOTT stands at the forefront of this challenge and reconstructs complete mutational landscapes of proteins, enables the identification of protein regions most vulnerable to mutations and assigns scores to individual mutations, assisting pathologists in evaluating the pathogenic potential of missense variants. PRESCOTT categorizes these variants into three distinct classes: Benign, Pathogenic, or Variants of Uncertain Significance (VUS). The model leverages protein sequences across millions of species, advanced protein structural models, and extensive genomic and exomic data from diverse human populations. By using only sequence and structural information, it significantly improves on current standards for predicting mutations in human proteins and matches AlphaMissense performance, which incorporates allele frequency data in its analysis. By including population-specific allele frequencies, PRESCOTT excels in genome-scale score separation of ClinVar benign and pathogenic variants and surpasses AlphaMissense in analyzing the ACMG reference human dataset and the over 1800 proteins from the Human Protein Dataset. Its efficacy is particularly notable in autoinflammatory diseases, accurately predicting pathogenic gain-of-function missense mutations, a task known for its difficulty. Efficiency and accessibility are key aspects of PRESCOTT. The user-friendly PRESCOTT webserver facilitates mutation effect calculations on any protein and protein variants. The server hosts a Comprehensive Human Protein Database for over 19,000 human proteins, based on sequences and structures, ready for a customized allele population analysis. Additionally, the tool provides open access to all intermediate scores, ensuring interpretability and transparency in variant analysis. PRESCOTT is a significant stride forward in the field of genomic medicine, offering unparalleled insights into protein mutational impacts.