Sritharan Sujith, Versini Raphaelle, Petit Jules D, Bayer Emmanuelle E, Taly Antoine
Laboratoire de Biochimie Théorique, CNRS (UPR9080), Université Paris Cité, Paris, France.
Laboratoire de Biogenèse Membranaire, Unité Mixte de Recherche 5200, Université de Bordeaux, Centre National de la Recherche Scientifique, Villenave d'Ornon, France.
PLoS One. 2025 Jul 15;20(7):e0326993. doi: 10.1371/journal.pone.0326993. eCollection 2025.
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 (DL). 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 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. We then used physics-based molecular dynamics 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 multiple conformations. The membrane-embedded region contains two helical pairs: HP1 (TM1-TM2) and HP2 (TM3-TM4). Deep learning predictions revealed three distinct types of inter-helical contact interfaces: ESMFold, AlphaFold-Multimer, trRosetta, and RoseTTAFold consistently predicted a TM2-TM3 contact; AlphaFold2 did not predict any contact between these two helical pairs, while OmegaFold instead suggested a TM1-TM4 interface. Our physics-based coarse-grained simulations not only confirmed the contacts predicted by these models but also revealed a broader conformational landscape. In particular, structural clustering identified five distinct conformational clusters, with additional and more extensive inter-helical contacts not captured by the deep learning predictions. 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.
植物中的多C2结构域和跨膜区蛋白（MCTPs）已被确定为胞间连丝细胞质桥的重要功能和结构成分，而胞间连丝对于细胞间通讯至关重要。MCTPs是与内质网（ER）相关的蛋白，包含三到四个C2结构域和两个跨膜区。在本研究中，我们使用了几种基于深度学习（DL）的预测方法，创建了拟南芥MCTP4内质网锚定跨膜区（TMR）结构域的结构模型。这个对驱动内质网关联至关重要的区域呈现出复杂的结构域组织，并且在很大程度上仍然未知。我们的研究表明，使用单一的深度学习方法来预测膜蛋白的结构可能具有挑战性。我们的模型呈现了由不同深度学习方法提供的MCTP4结构的三种不同构象，表明该蛋白构象景观的潜在复杂性。然后，我们使用基于物理的分子动力学模拟来探索MCTPs的TMR在脂质双层中的行为。我们发现MCTP4的TMR不是刚性的，而是可以采用多种构象。膜嵌入区域包含两对螺旋：HP1（TM1-TM2）和HP2（TM3-TM4）。深度学习预测揭示了三种不同类型的螺旋间接触界面：ESMFold、AlphaFold-Multimer、trRosetta和RoseTTAFold一致预测了TM2-TM3接触；AlphaFold2没有预测这两对螺旋之间的任何接触，而OmegaFold则暗示了TM1-TM4界面。我们基于物理的粗粒度模拟不仅证实了这些模型预测的接触，还揭示了更广泛的构象景观。特别是，结构聚类确定了五个不同的构象簇，具有深度学习预测未捕获的额外且更广泛的螺旋间接触。这些发现强调了预测蛋白质结构的复杂性。我们了解到，结合不同的方法，如深度学习和模拟，可以增强我们对复杂蛋白质的理解。
