This dataset brings together computational models and simulation results that explore how lipid-based molecules affect the human LGR6 receptor, a G protein-coupled receptor (GPCR) involved in inflammation. It includes structural predictions, molecular docking, and molecular dynamics simulations that show how different lipid molecules interact with LGR6 in both its active and inactive forms.

The receptor structures were predicted using AlphaFold2 and AlphaFold3, while docking results were produced with AutoDock Vina and GOLD. Long molecular dynamics simulations were carried out using Amber24, with each system run five times to ensure consistency and reliability.

The dataset features several lipid mediators, including Maresin 1, Protectin D1, Leukotriene B4, and DHA-derived molecules, allowing researchers to see how small chemical changes in these lipids can influence LGR6’s shape and activity. It also includes results from protein structure network analyses, which map out how different parts of the receptor communicate during the simulations.

All files are provided in common, open formats (such as PDB, CIF, VMD, and Amber topology/coordinate files), making them easy to visualize, compare, or reuse for further studies. Altogether, this collection offers a detailed, reproducible view of how bioactive lipids may regulate LGR6 and contribute to the resolution of inflammation.

This dataset contains molecular docking and molecular dynamics simulation results related to the activation of the LGR6 receptor by lipid mediators involved in inflammation resolution. The following sections describe the structure and contents of the dataset.

Amber, 24

AlphaFold, 2

AlphaFold, 3

METHODOLOGICAL INFORMATION (1) Description of Methods Used for Collection–Generation of Data: The dataset was produced using a combination of molecular docking, molecular dynamics (MD), and AI-based protein structure prediction. AlphaFold2 and AlphaFold3 were employed for structure and binding site prediction of the LGR6 receptor, respectively. Molecular docking was carried out using AutoDock Vina and GOLD, followed by extensive MD simulations with Amber24 to investigate the stability and conformational dynamics of receptor–ligand complexes. (2) Methods for Processing the Data Trajectory analysis was performed using cpptraj and in-house Python/Bash scripts for automation, including extraction of RMSD and hydrogen-bond data. (3) Instrument- or Software-Specific PlayMolecule Server for hydrogen atom topology; AlphaFold2 and AlphaFold3 for receptor structure and binding site prediction; AutoDock Vina and GOLD for molecular docking; Amber24 for molecular dynamics simulations; Chimera, ChimeraX and VMD for structure and trajectory visualization. All software used is publicly available, though GOLD requires a licensed institutional version. (4) Instruments, Calibration, and Standards Information Simulations were executed on high-performance computing clusters and GPUs. MD simulations utilized NVIDIA RTX 4090 and H100 GPUs for enhanced computational speed, while large-scale runs were performed at CSUC and Picard (Physical Chemistry) facilities. Charmm36m force field parameters and the TIP3P water model were used for system equilibration and production. (5) Environmental or Experimental Conditions Simulations were conducted under constant temperature (310.15 K) and pressure (1 atm) in explicit water using the TIP3P model and periodic boundary conditions. Systems were equilibrated using the NPT ensemble before production runs. (6) Quality-Assurance Procedures Performed on the Data Each system was simulated across five independent replicas exceeding 2000 ns to ensure convergence and reproducibility. Structural stability was monitored through RMSD analyses.