Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases

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.

Automated summary

The integration of human disease-related mutation data into protein structures is crucial for accurately assessing the impact of the mutation. Identifying the isoforms onto which mutations should be mapped is a key preliminary step in structural modelling due to the presence of functionally different protein isoforms from the same gene. To automate this challenging task for large datasets, the MoNvIso code is presented, which identifies the most suitable isoform for computational modelling by balancing mutation coverage and template availability for structural model construction of both wild-type and variant isoforms.