  Accurate prediction of binding free energies is critical to streamlining the
drug development and protein design process. With the advent of GPU
acceleration, absolute alchemical methods, which simulate the removal of ligand
electrostatics and van der Waals interactions with the protein, have become
routinely accessible and provide a physically rigorous approach that enables
full consideration of flexibility and solvent interaction. However, standard
explicit solvent simulations are unable to model protonation or electronic
polarization changes upon ligand transfer from water to the protein interior,
leading to inaccurate prediction of binding affinities for charged molecules.
Here, we perform extensive simulation totaling ~540 $\mu$s to benchmark the
impact of modeling conditions on predictive accuracy for absolute alchemical
simulations. Binding to urokinase plasminogen activator (UPA), a protein
frequently overexpressed in metastatic tumors, is evaluated for a set of ten
inhibitors with extended flexibility, highly charged character, and titratable
properties. We demonstrate that the alchemical simulations can be adapted to
utilize the MBAR/PBSA method to improve the accuracy upon incorporating
electronic polarization, highlighting the importance of polarization in
alchemical simulations of binding affinities. Comparison of binding energy
prediction at various protonation states indicates that proper electrostatic
setup is also crucial in binding affinity prediction of charged systems,
prompting us to propose an alternative binding mode with protonated ligand
phenol and Hid-46 at the binding site, a testable hypothesis for future
experimental validation.
