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

doi:10.3389/fchem.2022.1059593

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

Front Chem. 2023 Jan 9:10:1059593. doi: 10.3389/fchem.2022.1059593. eCollection 2022.

Authors

Francesco Oliva^{1

2}, Francesco Musiani³, Alejandro Giorgetti^{2

4}, Silvia De Rubeis^{5

6

7

8}, Oksana Sorokina⁹, Douglas J Armstrong^{2

9

10}, Paolo Carloni^{2

11

12}, Paolo Ruggerone¹

Affiliations

¹ Department of Physics, University of Cagliari, Monserrato (CA), Italy.
² Institute of Neuroscience and Medicine INM-9, Institute for Advanced Simulations IAS-5, Forschungszentrum Jülich, Jülich, Germany.
³ Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
⁴ Department of Biotechnology, University of Verona, Verona, Italy.
⁵ Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
⁶ Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
⁷ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
⁸ Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
⁹ The School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
¹⁰ Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, United Kingdom.
¹¹ Department of Physics, RWTH Aachen University, Aachen, Germany.
¹² JARA-Institute: Molecular Neuroscience and Neuroimaging, Institute for Neuroscience and Medicine INM-11/JARA-BRAIN Institute JBI-2, Forschungszentrum Jülich GmbH, Jülich, Germany.

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.

Keywords: diseases; isoform identification; molecular modelling; mutations; proteins.

Grants and funding

SR received a Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation. JA and OS received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).