Journal Article

FZJ-2024-02219

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases

Oliva, F. ; Musiani, F. ; Giorgetti, A.FZJ* ; De Rubeis, S. ; Sorokina, O. ; Armstrong, D. J. ; Carloni, P.FZJ* ; Ruggerone, P. (Corresponding author)

2023
Frontiers Media Lausanne

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Please use a persistent id in citations: doi:10.3389/fchem.2022.1059593  doi:10.34734/FZJ-2024-02219

Abstract: The seamless integration of human disease-related mutation data into proteinstructures is an essential component of any attempt to correctly assess theimpact of the mutation. The key step preliminary to any structural modelling isthe identification of the isoforms onto which mutations should be mapped dueto there being several functionally different protein isoforms from the samegene. To handle large sets of data coming from omics techniques, thischallenging task needs to be automatized. Here we present the MoNvIso(Modelling eNvironment for Isoforms) code, which identifies the most usefulisoform for computational modelling, balancing the coverage of mutations ofinterest and the availability of templates to build a structural model of both thewild-type isoform and the related variants.

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Contributing Institute(s):

1. Computational Biomedicine (IAS-5)
2. Computational Biomedicine (INM-9)

Research Program(s):

1. 5241 - Molecular Information Processing in Cellular Systems (POF4-524) (POF4-524)

Appears in the scientific report 2024

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