Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics

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 sim-
ulated molecular dynamics, including the prediction of their behavior in the pres-
ence 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 eval-
uated 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 ̊A and 0.156 ̊A respec-
tively. This work shows the potential of LSTM to predict the molecular dynamics
of GPRCs.