Understanding the Structure-Activity Relationship through Density Functional Theory: A Simple Method Predicts Relative Binding Free Energies of Metalloenzyme Fragment-like Inhibitors

Despite being involved in several human diseases, metalloenzymesare targeted by a small percentage of FDA-approved drugs. Developmentof novel and efficient inhibitors is required, as the chemical spaceof metal binding groups (MBGs) is currently limited to four main classes.The use of computational chemistry methods in drug discovery has gainedmomentum thanks to accurate estimates of binding modes and bindingfree energies of ligands to receptors. However, exact predictionsof binding free energies in metalloenzymes are challenging due tothe occurrence of nonclassical phenomena and interactions that commonforce field-based methods are unable to correctly describe. In thisregard, we applied density functional theory (DFT) to predict thebinding free energies and to understand the structure-activityrelationship of metalloenzyme fragment-like inhibitors. We testedthis method on a set of small-molecule inhibitors with different electronicproperties and coordinating two Mn2+ ions in the bindingsite of the influenza RNA polymerase PA(N) endonuclease.We modeled the binding site using only atoms from the first coordinationshell, hence reducing the computational cost. Thanks to the explicittreatment of electrons by DFT, we highlighted the main contributionsto the binding free energies and the electronic features differentiatingstrong and weak inhibitors, achieving good qualitative correlationwith the experimentally determined affinities. By introducing automateddocking, we explored alternative ways to coordinate the metal centersand we identified 70% of the highest affinity inhibitors. This methodologyprovides a fast and predictive tool for the identification of keyfeatures of metalloenzyme MBGs, which can be useful for the designof new and efficient drugs targeting these ubiquitous proteins.

Automated summary

In this study, density functional theory (DFT) was used to predict the binding free energies of metalloenzyme fragment-like inhibitors. By introducing automated docking, high affinity inhibitors were identified. This methodology provides a fast and predictive tool for the identification of key features of metalloenzyme MBGs, which can be useful for the design of new and efficient drugs targeting these ubiquitous proteins.