Prediction of A. thaliana’s MCTP4 Structure using Deep Learning-Based tools and Exploration of Transmembrane domain Dynamics using Coarse-Grained Molecular Dynamics Simulations
| dc.rights.license | open | en_US |
| dc.contributor.author | SRITHARAN, Sujith | |
| dc.contributor.author | VERSINI, Raphaelle | |
| dc.contributor.author | PETIT, Jules | |
| hal.structure.identifier | Laboratoire de biogenèse membranaire [LBM] | |
| dc.contributor.author | BAYER, Emmanuelle | |
| hal.structure.identifier | Laboratoire de biochimie théorique [Paris] [LBT (UPR_9080)] | |
| dc.contributor.author | TALY, Antoine | |
| dc.date.accessioned | 2024-05-10T09:15:26Z | |
| dc.date.available | 2024-05-10T09:15:26Z | |
| dc.date.created | 2024-01-26 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/199736 | |
| dc.description.abstractEn | Abstract Multiple C2 Domains and Transmembrane region Proteins (MCTPs) in plants have been identified as important functional and structural components of plasmodesmata cytoplasmic bridges, which are vital for cell-cell communication. MCTPs are endoplasmic reticulum (ER)-associated proteins which contain three to four C2 domains and two transmembrane regions. In this study, we created structural models of Arabidopsis MCTP4 ER-anchor transmembrane region (TMR) domain using several prediction methods based on deep learning. This region, critical for driving ER association, presents a complex domain organization and remains largely unknown. Our study demonstrates that using a single deep-learning method to predict the structure of membrane proteins can be challenging. Our deep learning models presented three different conformations for the MCTP4 structure, provided by different deep learning methods, indicating the potential complexity of the protein’s conformational landscape. For the first time, we used simulations to explore the behaviour of the TMR of MCTPs within the lipid bilayer. We found that the TMR of MCTP4 is not rigid, but can adopt various conformations including some not identified by deep learning tools. These findings underscore the complexity of predicting protein structures. We learned that combining different methods, such as deep learning and simulations, enhances our understanding of complex proteins. | |
| dc.description.sponsorship | Diviser et connecter: mise en place de la communication intercellulaire pendant la division cellulaire - ANR-21-CE13-0016 | en_US |
| dc.description.sponsorship | Dynamique des membranes transductrices d'énergie : biogénèse et organisation supramoléculaire. - ANR-11-LABX-0011 | en_US |
| dc.language.iso | EN | en_US |
| dc.title.en | Prediction of A. thaliana’s MCTP4 Structure using Deep Learning-Based tools and Exploration of Transmembrane domain Dynamics using Coarse-Grained Molecular Dynamics Simulations | |
| dc.type | Document de travail - Pré-publication | en_US |
| dc.identifier.doi | 10.1101/2023.08.04.552001 | en_US |
| dc.subject.hal | Chimie | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Biochimie, Biologie Moléculaire | en_US |
| bordeaux.hal.laboratories | Laboratoire de Biogenèse Membranaire (LBM) - UMR 5200 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | CNRS | en_US |
| bordeaux.import.source | hal | |
| hal.identifier | hal-04472182 | |
| hal.version | 1 | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | hal | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.subtype | Prepublication/Preprint | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=SRITHARAN,%20Sujith&VERSINI,%20Raphaelle&PETIT,%20Jules&BAYER,%20Emmanuelle&TALY,%20Antoine&rft.genre=preprint |
