Oliva Francesco, Musiani Francesco, Giorgetti Alejandro, De Rubeis Silvia, Sorokina Oksana, Armstrong Douglas J, Carloni Paolo, Ruggerone Paolo
Department of Physics, University of Cagliari, Monserrato (CA), Italy.
Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany.
Front Chem. 2023 Jan 9;10:1059593. doi: 10.3389/fchem.2022.1059593. eCollection 2022.
The seamless integration of human disease-related mutation data into protein structures is an essential component of any attempt to correctly assess the impact of the mutation. The key step preliminary to any structural modelling is the identification of the isoforms onto which mutations should be mapped due to there being several functionally different protein isoforms from the same gene. To handle large sets of data coming from omics techniques, this challenging task needs to be automatized. Here we present the MoNvIso (Modelling eNvironment for Isoforms) code, which identifies the most useful isoform for computational modelling, balancing the coverage of mutations of interest and the availability of templates to build a structural model of both the wild-type isoform and the related variants.
将人类疾病相关突变数据无缝整合到蛋白质结构中，是正确评估突变影响的任何尝试的重要组成部分。任何结构建模之前的关键步骤是确定应将突变映射到哪些异构体上，因为同一基因存在几种功能不同的蛋白质异构体。为了处理来自组学技术的大量数据，这项具有挑战性的任务需要自动化。在这里，我们展示了MoNvIso（异构体建模环境）代码，它可以识别出对计算建模最有用的异构体，在感兴趣的突变覆盖率和构建野生型异构体及相关变体结构模型模板的可用性之间取得平衡。
