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

TL;DR

This study evaluates LSTM neural networks for predicting the 3D molecular dynamics trajectories of GPCR amino acids, demonstrating robust performance comparable to current state-of-the-art methods.

Contribution

It introduces LSTM models for predicting GPCR amino acid positions in molecular dynamics, exploring different positional transformations and showing promising results.

Findings

LSTM models achieved low MAE and RMSD in predicting amino acid positions.

Mass center of amino acids yielded the best prediction accuracy.

LSTM performance is comparable to state-of-the-art in non-dynamic 3D predictions.

Abstract

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 {\AA} and 0.156 {\AA} respectively. This work shows the potential of LSTM to predict the molecular dynamics of GPRCs.