Per- and polyfluoroalkyl substances (PFAS) can bioaccumulate and may increase the risk of cancer development. Clinical studies report that cholestyramine treatment lowers serum PFAS concentrations, but the molecular energetics, interactions and underlying mechanisms of PFAS exchange with the resin remain incompletely understood. Here, we present a quantum perspective to define the mechanism of PFAS anion exchange with cholestyramine. Quantum chemistry calculations, including density functional theory (DFT), were used to evaluate chloride exchange for perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanoic acid (PFHxA), and 6:2 fluorotelomer carboxylic acid (6:2 FTCA) at an isolated ammonium site and in an extended styrene-divinylbenzene model. Single-site ion pairing did not make exchange with chloride thermodynamically favorable. Adding the divinylbenzene backbone lowered electronic exchange energies for all four PFAS, whereas the Gibbs free-energy effect varied among compounds. Energy decomposition analysis showed that electrostatic attraction was the largest stabilizing contribution, while added backbone contacts altered dispersion, exchange correlation and Pauli contributions. For PFOA, changing the bound state continuum from water to 1-octanol produced little net change in exchange free energy. Three separately seeded 10 ns molecular dynamics trajectories of a hydrated pVBTMA₁₂ oligomer showed persistent PFOA contact with changing headgroup and tail environments, although global structural convergence was not established. Together, these results support a molecular mechanism in which electrostatic ion pairing and PFAS backbone contacts jointly influence anion exchange favorability in simplified cholestyramine models. This mechanistic framework provides testable hypotheses for PFAS sequestration and identifies aromatic contact area and ammonium site geometry as design variables for future resin optimization. These findings connect quantum level interaction energetics with experimentally testable strategies for developing improved PFAS sequestrants. Computational data and code are archived in Zenodo, and a web-based graphical user interface tool is available online.

Metalloprotein docking is difficult because it requires precise preparation of the grid box, charge assignment, and metal coordination geometry. Many current graphical user interfaces (GUIs) do not support these steps well. Here, we present MBind v2026, a web-based GUI that combines AutoDock Vina, AutoGrid4 and AutoDock4 into a single workflow for standard docking and metalloprotein docking. The interface also has the optional machine learning (ML) docking program GNINA v1.0. The main validated metalloprotein workflow in MBind is based on AutoDock4Zn parameterization for zinc, while preliminary workflows for magnesium, iron, and copper are also included. MBind was benchmarked on eight zinc metalloproteins and eight nonmetalloprotein targets. Its performance was compared with ML pose prediction methods (GNINA v1.0, EquiBind v2026, TankBind v2026, and GAABind v2026), cofolding models (Boltz-2 v2026, and AlphaFold 3 v2026), ML affinity prediction tools (StructureNet v2026, GNNSeq v2026, and PLAIG v2026), and non-ML docking tools (SwissDock v2026, CB-Dock2 v2026, Webina v2026, 1-ClickDock v2026, and MolModa v1.01). Pose accuracy was measured by symmetry-aware ligand RMSD using DockRMSD v1.1 under redocking conditions with co-crystal-defined binding sites. Binding energy trends were evaluated by comparing docking scores with IC50-derived ΔG values using mean absolute error. On the zinc metalloprotein benchmark set, MBind produced a mean RMSD of 0.49 Å. On the nonmetalloprotein set, the mean RMSD was 0.62 Å. These results show that MBind can execute zinc metalloprotein and nonmetalloprotein docking workflows through a web-based interface, while the preliminary Mg, Fe, and Cu workflows require additional validation. The MBind GUI web-based platform is publicly available.