G protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one, through their activation path and focussing on specific receptor regions. Active and inactive states of the GPCRs are analysed in six scenarios involving APO, Full Agonist (BI 167107) and Partial Inverse Agonist (carazolol) of the receptor. Four Machine Learning models with increasing complexity in terms of neural network architecture are evaluated, and their results discussed. The best method achieves an overall RMSD lower than 0.139 Å and the transmembrane helices are the regions showing the minimum prediction errors and minimum relative movements of the protein.

G蛋白偶联受体（GPCR）是细胞膜蛋白的一个大超家族，作为细胞外信号的传递者发挥着重要的生理作用。通过细胞膜的信号传输取决于受体跨膜区域的构象变化，这使得这些区域的动力学研究特别重要。分子动力学 (MD) 模拟通过对生物大分子原子成分之间的相互作用进行建模，提供有关生物大分子的结构、动力学和生理功能的大量数据。在这项研究中，使用循环和卷积神经网络 (RNN) 模型，即长短期记忆 (LSTM)，通过其激活路径来预测两种 GPCR 状态的动态以及每种状态的三种特定模拟，并重点关注特定的受体区域。在涉及受体的 APO、完全激动剂 (BI 167107) 和部分反向激动剂 (卡拉洛尔) 的六种情况下分析 GPCR 的活性和非活性状态。对神经网络架构方面复杂性不断增加的四种机器学习模型进行了评估，并讨论了它们的结果。最佳方法的总体 RMSD 低于 0.139 Å，跨膜螺旋是显示最小预测误差和最小蛋白质相对运动的区域。