Arnaudi Matteo, Utichi Mattia, Tiberti Matteo, Papaleo Elena
Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100, Copenhagen, Denmark; Cancer Systems Biology, Section of Bioinformatics, Health and Technology Department, Technical University of Denmark, Lyngby, Denmark.
Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100, Copenhagen, Denmark.
Curr Opin Struct Biol. 2025 Apr;91:102994. doi: 10.1016/j.sbi.2025.102994. Epub 2025 Feb 27.
Missense variants can affect the severity of disease, choice of treatment, and treatment outcomes. While the number of known variants has been increasing at a rapid pace, available evidence of their clinical effect has been lagging behind, constituting a challenge for clinicians and researchers. Multiplexed assays of variant effects (MAVEs) are important to close the gap; nonetheless, computational predictions of pathogenicity are still often the only available data for scoring variants. Such methods are not designed to provide a mechanistic explanation for the effect of amino acid substitutions. To this purpose, we propose structure-based frameworks as ensemble methodologies, with each method tailored to predict a different aspect among those exerted by amino acid substitutions to link predicted pathogenicity to mechanistic indicators. We review available frameworks, as well as advancements in underlying structure-based methods that predict variant effects on several protein features, such as protein stability, biomolecular interactions, allostery, post-translational modifications, and more.
错义变体可影响疾病的严重程度、治疗选择及治疗结果。尽管已知变体的数量一直在快速增加，但其临床效应的现有证据却滞后了，这给临床医生和研究人员带来了挑战。变异效应多重分析（MAVEs）对于弥合这一差距很重要；尽管如此，致病性的计算预测仍然常常是对变体进行评分的唯一可用数据。此类方法并非旨在为氨基酸取代的效应提供机制解释。为此，我们提出基于结构的框架作为集成方法，每种方法都经过定制，以预测氨基酸取代所产生的不同方面，从而将预测的致病性与机制指标联系起来。我们回顾了可用的框架，以及基于结构的基础方法在预测变体对多种蛋白质特征（如蛋白质稳定性、生物分子相互作用、变构、翻译后修饰等）的影响方面的进展。
