论文标题
长期的短期记忆预测GPCR分子动力学中的3D氨基酸位置
Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics
论文作者
论文摘要
G蛋白偶联受体（GPCR）是真核细胞跨膜蛋白的大家族，负责众多生物学过程。从实际的角度来看，美国食品和药物管理局批准的药物中有34％以这些受体为目标。可以从模拟的分子动力学中分析它们，包括在存在药物的情况下对其行为的预测。在本文中，评估长期短期记忆网络（LSTM）的能力以学习和预测受体的分子动态轨迹。几种模型接受了受体的氨基酸的3D位置训练，考虑到氨基酸位置的不同转化，例如它们的质量中心，几何中心和每个氨基酸的$α$ - 碳的位置。通过平均平均误差（MAE）和根平方偏差（RMSD）评估了该位置预测的误差。 LSTM模型显示出强大的性能，结果与非动态3D预测中最新的结果相当。在分别为0.078Å和0.156Å的氨基酸的质量中心发现了最佳的MAE和RMSD值。这项工作表明了LSTM预测GPRC的分子动力学的潜力。
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 $α$--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.
