“Adapted Linear Interaction Energy”: A Structure-Based LIE Parametrization for Fast Prediction of Protein–Ligand Affinities

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.

Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging

Accurate calculations of free energies involved in small-molecule binding to a receptor are challenging. Interactions between ligand, receptor, and solvent molecules have to be described precisely, and a large number of conformational microstates has to be sampled, particularly for ligand binding to a flexible protein. Linear interaction energy models are computationally efficient methods that have found considerable success in the prediction of binding free energies. Here, we parametrize a linear interaction model for implicit solvation with coefficients adapted by ligand and binding site relative polarities in order to predict ligand binding free energies. Results obtained for a diverse series of ligands suggest that the model has good predictive power and transferability. We also apply implicit ligand theory and propose approximations to average contributions of multiple ligand-receptor poses built from a protein conformational ensemble and find that exponential averages require proper energy discrimination between plausible binding poses and false-positives (i.e., decoys). The linear interaction model and the averaging procedures presented can be applied independently of each other and of the method used to obtain the receptor structural representation.

Comprehending renin inhibitor's binding affinity using structure-based approaches

The performance of several structure-based design (SBD) approaches in predicting the binding affinity of diverse small molecule inhibitors co-crystallized to human renin was assessed to ascertain the modeling tool and method of choice required when dealing with structure-based lead optimization projects. Most of the SBD approaches investigated here were able to provide qualitative guidance, but quantitative accuracy as well as decisive discrimination between [in]actives is still not within reach. Such an outcome suggests that the current methods need improvement to capture the overall physics of the binding phenomenon for consistent applications in a lead optimization setting.