Deep Learning-Based Prediction of A. thaliana’s MCTP4 Structure and Exploration of Transmembrane Dynamics using Coarse-Grained Molecular Dynamics Simulations

Abstract

AbstractMultiple 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 endoplas-mic reticulum (ER)-associated proteins which contain three to four C2 domains and two transmembrane regions. In this study, we created structural models ofArabidop-sisMCTP4 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 mem-brane 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 vari-ous 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.