Name: Ligand pose prediction using autodock
Software: UCSF Chimera 1.11.2rc, DataWarrior 4.4.3 (OpenMolecules), Open-Source PyMOL 1.8.x (Schrödinger), Open Babel 2.4.1, AutoDock 2.4.6, PLIP server (Dresden University)
System Preparation Parameters: polar hydrogen protonation, Gasteiger charges assignment, grid maps of interaction calculation
System Preparation Method: Structural clustering was carried out on the 102 compounds with DataWarrior to obtain most representative scaffolds. The Protein Data Bank was serached for FXR receptors co-crystallised with ligands having similar scaffolds to those previously determined. The selected structures (3DCU and 3OKH) were prepared with the UCSF Chimera DockPrep script, taking care of histidines and carboxylic acids protonation states. Then non polar hydrogens were removed and Gasteiger charges assigned to both receptors and ligands.
Pose Prediction Parameters: docking using default parameters of genetic algorithm and generation of 250 models per compound
Pose Prediction Method: Autodock was run using default parametrization of Lamarckian genetic search algorithm to generate 250 docking poses for each compound. Each pose was analysed by visual inspection in Open-Source PyMOL, by computation of contacts between poses and receptors with PLIP server, and considering Autodock scoring results and relative orientations of isosteres. Top 5 conformations were kept for submission.