NEMAT: An Automated Nonequilibrium Free-Energy Framework for Predicting Ligand Affinity in Membrane Proteins

Abstract Quantifying the strength of small-molecule binding to proteins is essential for understanding biological functions and for advancing drug discovery. Computational free-energy methods can predict binding affinities, but membrane proteins remain challenging because of thermodynamic effects of the lipid bilayer. Alchemical free-energy calculations provide state-of-the-art accuracy, and recent nonequilibrium approaches offer improved efficiency and parallelization. However, no existing framework automates these nonequilibrium methods for membrane proteins or enables decomposition of binding free energy into membrane-partitioning and protein-specific components. Nonequilibrium membrane alchemical transformations (NEMAT) is an open-source framework that performs automated nonequilibrium alchemical transformations in water, membranes, and membrane-embedded protein environments. We show that NEMAT reproduces experimental binding energy trends for P2Y1 ligands with accuracy comparable to established equilibrium methods. NEMAT provides systematic control over critical simulation parameters to optimize the free-energy estimates. Its ability to dissect membrane and protein contributions supports mechanistically interpretable affinity predictions for membrane-embedded targets.