GPCR molecular dynamics forecasting using recurrent neural networks.

ABSTRACT: G protein-coupled receptors (GPCRs) are a large superfamily of cell membrane proteins that play an important physiological role as transmitters of extracellular signals. Signal transmission through the cell membrane depends on conformational changes in the transmembrane region of the receptor, which makes the investigation of the dynamics in these regions particularly relevant. Molecular dynamics (MD) simulations provide a wealth of data about the structure, dynamics, and physiological function of biological macromolecules by modelling the interactions between their atomic constituents. In this study, a Recurrent and Convolutional Neural Network (RNN) model, namely Long Short-Term Memory (LSTM), is used to predict the dynamics of two GPCR states and three specific simulations of each one,

SUBMITTER: Lopez-Correa JM 

PROVIDER: S-EPMC10684758 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature