Departamento de Microbiología, Inmunología, Biotecnología y Genética, Cátedra de Genética, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina.
Instituto de Inmunología, Genética y Metabolismo (INIGEM), Universidad de Buenos Aires - CONICET, Buenos Aires, Argentina.
Hum Mutat. 2020 Jan;41(1):81-102. doi: 10.1002/humu.23925. Epub 2019 Oct 14.
Massive parallel sequencing technologies are facilitating the faster identification of sequence variants with the consequent capability of untangling the molecular bases of many human genetic syndromes. However, it is not always easy to understand the impact of novel variants, especially for missense changes, which can lead to a spectrum of phenotypes. This study presents a custom-designed multistep methodology to evaluate the impact of novel variants aggregated in the genome aggregation database for the HBB, HBA2, and HBA1 genes, by testing and improving its performance with a dataset of previously described alterations affecting those same genes. This approach scored high sensitivity and specificity values and showed an overall better performance than sequence-derived predictors, highlighting the importance of protein conformation and interaction specific analyses in curating variant databases. This study also describes the strengths and limitations of these structural studies and allows identifying residues in the globin chains more prone to tolerate substitutions.
高通量测序技术正在加速鉴定序列变异体,从而能够阐明许多人类遗传综合征的分子基础。然而,理解新型变异体的影响并不总是那么容易,特别是对于错义突变,其可能导致一系列表型。本研究提出了一种定制的多步骤方法,通过使用先前描述的影响相同基因的数据集来测试和改进其性能,评估在 HBB、HBA2 和 HBA1 基因的基因组聚集数据库中聚集的新型变异体的影响。该方法具有较高的灵敏度和特异性值,总体性能优于序列衍生预测器,突出了在变异体数据库中进行蛋白质构象和相互作用特异性分析的重要性。本研究还描述了这些结构研究的优缺点,并能够确定球蛋白链中更耐受取代的残基。