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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 m...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868658/ https://www.ncbi.nlm.nih.gov/pubmed/36700074 http://dx.doi.org/10.3389/fchem.2022.1059593 |
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author | Oliva, Francesco Musiani, Francesco Giorgetti, Alejandro De Rubeis, Silvia Sorokina, Oksana Armstrong, Douglas J. Carloni, Paolo Ruggerone, Paolo |
author_facet | Oliva, Francesco Musiani, Francesco Giorgetti, Alejandro De Rubeis, Silvia Sorokina, Oksana Armstrong, Douglas J. Carloni, Paolo Ruggerone, Paolo |
author_sort | Oliva, Francesco |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9868658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98686582023-01-24 Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases Oliva, Francesco Musiani, Francesco Giorgetti, Alejandro De Rubeis, Silvia Sorokina, Oksana Armstrong, Douglas J. Carloni, Paolo Ruggerone, Paolo Front Chem Chemistry 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. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9868658/ /pubmed/36700074 http://dx.doi.org/10.3389/fchem.2022.1059593 Text en Copyright © 2023 Oliva, Musiani, Giorgetti, De Rubeis, Sorokina, Armstrong, Carloni and Ruggerone. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Oliva, Francesco Musiani, Francesco Giorgetti, Alejandro De Rubeis, Silvia Sorokina, Oksana Armstrong, Douglas J. Carloni, Paolo Ruggerone, Paolo Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases |
title | Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases |
title_full | Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases |
title_fullStr | Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases |
title_full_unstemmed | Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases |
title_short | Modelling eNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases |
title_sort | modelling environment for isoforms (monviso): a general platform to predict structural determinants of protein isoforms in genetic diseases |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868658/ https://www.ncbi.nlm.nih.gov/pubmed/36700074 http://dx.doi.org/10.3389/fchem.2022.1059593 |
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