Improved QM/MM Linear-Interaction Energy Model for Substrate Recognition in Zinc-Containing Metalloenzymes.

One of the essential challenges in the description of receptor-drug interactions in the presence of various polyvalent cations (such as zinc, magnesium, or iron) is the accurate assessment of the electronic effects due to cofactor binding. The effects can range from partial electronic polarization of the proximal atoms in a receptor and bound substrate to long-range effects related to partial charge transfer and electronic delocalization effects between the cofactor and the drug. Here, we examine the role of the explicit account for electronic effects for a panel of small-molecule inhibitors binding to the zinc-aminopeptidase PfA-M1, an essential target for antimalarial drug development. Our study on PfA-M1:inhibitor interactions at the QM level reveals that the partial charge and proton transfer due to bound zinc ion are important mechanisms in the inhibitors' recognition and catalysis. The combination of classical MD simulations with a posteriori QM/MM corrections with novel DFTB parameters for the zinc cation and the linear-interaction energy (LIE) approach offers by far the most accurate estimates for the PfA-M1:inhibitor binding affinities, opening the door for future inhibitor design.

Improving the LIE Method for Binding Free Energy Calculations of Protein-Ligand Complexes.

In this work, we introduced an improved linear interaction energy (LIE) method parameterization for computations of protein­ligand binding free energies. The protocol, coined LIE-D, builds on the linear relationship between the empirical coefficient γ in the standard LIE scheme and the D parameter, introduced in our work. The D-parameter encompasses the balance (difference) between electrostatic (polar) and van der Waals (nonpolar) energies in protein­ligand complexes. Leave-one-out cross-validation showed that LIE-D reproduced accurately the absolute binding free energies for our training set of protein­ligand complexes (<|error|> = 0.92 kcal/mol, SDerror = 0.66 kcal/mol, R(2) = 0.90, QLOO(2) = 0.89, and sPRESS(LOO) = 1.28 kcal/mol). We also demonstrated LIE-D robustness by predicting accurately the binding free energies for three different protein­ligand systems outside the training data set, where the electrostatic and van der Waals interaction energies were calculated with different force fields.