Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases.
diseases
isoform identification
molecular modelling
mutations
proteins
Journal
Frontiers in chemistry
ISSN: 2296-2646
Titre abrégé: Front Chem
Pays: Switzerland
ID NLM: 101627988
Informations de publication
Date de publication:
2022
2022
Historique:
received:
01
10
2022
accepted:
06
12
2022
entrez:
26
1
2023
pubmed:
27
1
2023
medline:
27
1
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36700074
doi: 10.3389/fchem.2022.1059593
pii: 1059593
pmc: PMC9868658
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1059593Informations de copyright
Copyright © 2023 Oliva, Musiani, Giorgetti, De Rubeis, Sorokina, Armstrong, Carloni and Ruggerone.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor SP declared a past co-authorship with the author AG.
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