Molecular Dynamics forecasting of transmembrane Regions in GPRCs by Recurrent Neural Networks
| Autor: | López Correa, Juan Manuel, König, Caroline, Vellido Alcacena, Alfredo |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial, Facultat d'Informàtica de Barcelona, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
| Rok vydání: | 2022 |
| Předmět: |
Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC]
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC] Recurrent neural networks Machine learning Aprenentatge automàtic Deep learning Proteïnes G Dinàmica molecular Molecular dynamics LSTM GPCRs G proteins |
| Zdroj: | 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). |
| DOI: | 10.1109/bhi56158.2022.9926945 |
| Popis: | G protein-coupled receptors are a large super-family of cell membrane proteins that play an important physiological role as transmitters of extra-cellular signals. Signal transmission through the cell membrane depends on the conformational changes of the transmembrane region of the receptor and the investigation of the dynamics in these regions is therefore key. Molecular Dynamics (MD) simulations can provide information of the receptor conformational states at the atom level and machine learning (ML) methods can be useful for the analysis of these data. In this paper, Recurrent Neural Networks (RNNs) are used to evaluate whether the MD can be modeled focusing on the different regions of the receptor (intra-cellular, extra-cellular and each transmembrane regions (TM)). The best results, as measured by root-mean-square deviation (RMSD), are 0.1228 Å for TM4 of the 2rh1 (inactive state) and 0.1325 Å for TM4 of the 3p0g (active state), which are comparable to the state-of-the-art in non-dynamic 3-D predictions, showing the potential of the proposed approach. This work is funded by Spanish PID2019-104551RB-I00 research project and by the PRE2020-092428 Ph.D. training program, through the Ministry Science and Innovation. |
| Databáze: | OpenAIRE |
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