Predicting lipid and ligand binding sites in TRPV1 channel by molecular
dynamics simulation and machine learning
Abstract
As a key cellular sensor, the TRPV1 channel undergoes a gating
transition from a closed state to an open state in response to many
physical and chemical stimuli. This transition is regulated by
small-molecule ligands including lipids and various
agonists/antagonists, but the underlying molecular mechanisms remain
obscure. Thanks to recent revolution in cryo-electron microscopy, a
growing list of new structures of TRPV1 and other TRPV channels have
been solved in complex with various ligands including lipids. Toward
elucidating how ligand binding correlates with TRPV1 gating, we have
performed extensive molecular dynamics simulations (with cumulative time
of 20 μs), starting from high-resolution structures of TRPV1 in both the
closed and open states. By comparing between the open and closed state
ensembles, we have identified state-dependent binding sites for
small-molecule ligands in general and lipids in particular. We further
use machine learning to predict top ligand-binding sites as important
features to classify the closed vs open states. The predicted binding
sites are thoroughly validated by matching homologous sites in all
structures of TRPV channels bound to lipids and other ligands, and with
previous functional/mutational studies of ligand binding in TRPV1. Taken
together, this study has integrated rich structural, dynamic, and
functional data to inform future design of small-molecular drugs
targeting TRPV1.