Despite being involved in several human diseases, metalloenzymes
are targeted by a small percentage of FDA-approved drugs. Development
of novel and efficient inhibitors is required, as the chemical space
of metal binding groups (MBGs) is currently limited to four main classes.
The use of computational chemistry methods in drug discovery has gained
momentum thanks to accurate estimates of binding modes and binding
free energies of ligands to receptors. However, exact predictions
of binding free energies in metalloenzymes are challenging due to
the occurrence of nonclassical phenomena and interactions that common
force field-based methods are unable to correctly describe. In this
regard, we applied density functional theory (DFT) to predict the
binding free energies and to understand the structure–activity
relationship of metalloenzyme fragment-like inhibitors. We tested
this method on a set of small-molecule inhibitors with different electronic
properties and coordinating two Mn2+ions in the binding
site of the influenza RNA polymerase PANendonuclease.
We modeled the binding site using only atoms from the first coordination
shell, hence reducing the computational cost. Thanks to the explicit
treatment of electrons by DFT, we highlighted the main contributions
to the binding free energies and the electronic features differentiating
strong and weak inhibitors, achieving good qualitative correlation
with the experimentally determined affinities. By introducing automated
docking, we explored alternative ways to coordinate the metal centers
and we identified 70% of the highest affinity inhibitors. 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.