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

arXiv: https://arxiv.org/abs/2207.05682 · HTML: https://mlsanity.com/paper/2207.05682/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 10 of 10)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction](https://mlsanity.com/paper/1801.02143/) (2018)
- [Image Captioning with Deep Bidirectional LSTMs](https://mlsanity.com/paper/1604.00790/) (2016)
- [3DMolNet: A Generative Network for Molecular Structures](https://mlsanity.com/paper/2010.06477/) (2020)
- [Deep Recurrent Neural Network for Protein Function Prediction from Sequence](https://mlsanity.com/paper/1701.08318/) (2017)
- [A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer](https://mlsanity.com/paper/2107.06573/) (2021)
- [Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations](https://mlsanity.com/paper/1909.10124/) (2019)

## Abstract (arXiv)

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