G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell
transmembrane proteins, responsible for numerous biological processes. From a
practical viewpoint around 34\% of the drugs approved by the US Food and Drug
Administration target these receptors. They can be analyzed from their
simulated molecular dynamics, including the prediction of their behavior in the
presence of drugs. In this paper, the capability of Long Short-Term Memory
Networks (LSTMs) are evaluated to learn and predict the molecular dynamic
trajectories of a receptor. Several models were trained with the 3D position of
the amino acids of the receptor considering different transformations on the
position of the amino acid, such as their centers of mass, the geometric
centers and the position of the $\alpha$--carbon for each amino acid. The error
of the prediction of the position was evaluated by the mean average error (MAE)
and root-mean-square deviation (RMSD). The LSTM models show a robust
performance, with results comparable to the state-of-the-art in non-dynamic 3D
predictions. The best MAE and RMSD values were found for the mass center of the
amino acids with 0.078 Å and 0.156 Å respectively. This work shows the
potential of LSTM to predict the molecular dynamics of GPRCs.

本研究评估了长短时记忆网络 （LSTM）学习和预测受体的分子动态轨迹的能力。实验表明，该方法在分析 G 蛋白偶联受体分子动态时表现出强大的性能，可用于药物预测及蛋白质结构分析等领域。