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

Francesco Oliva, Francesco Musiani, Alejandro Giorgetti, Silvia De Rubeis, Oksana Sorokina, Douglas J. Armstrong, Paolo Carloni, Paolo Ruggerone

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1059593
JournalFrontiers in Chemistry
Volume10
DOIs
StatePublished - 9 Jan 2023

Keywords

  • diseases
  • isoform identification
  • molecular modelling
  • mutations
  • proteins

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