Tekpinar Mustafa, David Laurent, Henry Thomas, Carbone Alessandra
Department of Computational, Quantitative and Synthetic Biology (CQSB), Sorbonne Université, CNRS, IBPS, UMR 7238, Paris, 75005, France.
Centre International de Recherche en Infectiologie (CIRI), Inserm U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Univ Lyon, Lyon, 69007, France.
Genome Biol. 2025 May 6;26(1):113. doi: 10.1186/s13059-025-03581-y.
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.
预测点突变的功能影响是基因组学中的一项关键挑战。PRESCOTT重建完整的突变图谱，识别突变敏感区域，并将错义变异分类为良性、致病性或意义未明的变异。利用蛋白质序列、结构模型和特定人群的等位基因频率，PRESCOTT在对ClinVar变异、ACMG数据集以及来自人类蛋白质数据集的1800多种蛋白质进行分类方面超越了现有方法。其在线服务器便于对任何蛋白质和变异进行突变效应预测，还包括一个拥有超过19000种人类蛋白质的数据库，可供进行特定人群分析。对残基特异性评分的开放访问为基因组医学提供了透明度和有价值的见解。
