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@ARTICLE{Oliva:1024532,
author = {Oliva, Francesco and Musiani, Francesco and Giorgetti,
Alejandro and De Rubeis, Silvia and Sorokina, Oksana and
Armstrong, Douglas J. and Carloni, Paolo and Ruggerone,
Paolo},
title = {{M}odelling e{N}vironment for {I}soforms ({M}o{N}v{I}so):
{A} general platform to predict structural determinants of
protein isoforms in genetic diseases},
journal = {Frontiers in Chemistry},
volume = {10},
issn = {2296-2646},
address = {Lausanne},
publisher = {Frontiers Media},
reportid = {FZJ-2024-02219},
pages = {1059593},
year = {2023},
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.},
cin = {IAS-5 / INM-9},
ddc = {540},
cid = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121},
pnm = {5241 - Molecular Information Processing in Cellular Systems
(POF4-524)},
pid = {G:(DE-HGF)POF4-5241},
typ = {PUB:(DE-HGF)16},
pubmed = {36700074},
UT = {WOS:000921373000001},
doi = {10.3389/fchem.2022.1059593},
url = {https://juser.fz-juelich.de/record/1024532},
}